• Journal of the Korea Institute of Building Construction
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• v.22 no.4
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• pp.403-414
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• 2022

Many studies have been conducted on asset management of public facilities, as the importance of such management has been increasing. This basic study aims to present strategies for the practical use of public asset management, and seeks to propose efficient management and utilization measures from a cost perspective by comparing and analyzing the importance and impact relationship between cost items for public asset management. In this study, 19 sub-items and the top 4 items were chosen by deriving cost factors based on the previous literature. A survey was conducted, and the results of the survey were analyzed by using the Analytic Hierarchy Process(AHP) and Interpretive Structural Modeling (ISM) methods. The AHP was used to derive the priority between items, and ISM was used to identify major groups and mutual influences. As a result, those items showing both high priority and high importance, such as user cost, dismantling/disposal cost, replacement cost, maintenance/repair cost, etc. are determined as priority items to be considered for public asset management of public facilities. Also, it is necessary to minimize the impact on other items in public asset management by those items which are impacted less by other items but have significant impact on the items such as initial construction costs, conceptual design costs, construction costs, and supervision costs. It is expected that the results and analysis methods presented in this study can be used to provide strategies for asset management of public facilities.

• Smart Media Journal
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• v.11 no.6
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• pp.37-50
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• 2022

This proposes a direction to build and provide an exhibition space based on the metaverse platform so that artists can independently open their own exhibitions in an online environment. Although many artists are produced every year, offline exhibitions are becoming difficult due to not many art galleries, and offline exhibitions are becoming impossible due to Corona, which began at the end of 2019, and online exhibitions are emerging as an alternative. We analyze cases of existing online exhibition methods and visit online exhibition methods such as web, video, and virtual reality and metaverse environment then present a plan for individual exhibitions in consideration of the recent metaverse environment. The proposed metaverse-based personal exhibition method is structured so that artists can construct a space on the metaverse and place their works, and then viewers can freely take a look on it from a remote location. Based on the proposed exhibition direction, the representative metaverse platform was applied to confirm the characteristics and possibilities of exhibition space and composition of works and users' exhibition experience. In the face of the rise of online exhibitions, space can be constructed in the direction the artist pursues in online exhibitions as well as offline exhibitions, but also online exhibitions, and hopes that online exhibitions will become another genre of exhibitions rather than incidental after the end of Covid-19.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.67-76
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• 2023

COVID-19, which occurred in 2019, has a strong contagious power, has serious symptoms of infection and after-effects, and death in severe cases depending on the underlying disease and symptoms. As COVID-19 is highly contagious, in Korea, screening clinics have been set up across the country to determine whether or not to be positive for COVID-19 and isolate the infected to prevent the spread of COVID-19. However, there are cases where COVID-19 test applicants flock to screening clinics and cannot receive tests due to longer waiting times, and there is a risk that secondary infections may occur in the atmosphere. In this study, the reservation and notification system can be applied from the existing screening care system to solve spatial constraints, reducing waiting time with screening appointments, and solving population bottlenecks to screening clinics. Taking the COVID-19 pandemic as an experience, we propose a system that can present directions in future pandemic situations. To process real-time data, we use Google's Firebase to use Realtime Database in the cloud environment. Because a real-time database is used, users can check the status of screening clinics in real time through the app, make reservations, and receive notifications about test reservations.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.330-340
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• 2022

We deduced the proper estimation methodology for the amount of carbon sequestration by damaged trees for Environmental Impact Assessment (EIA). The nine development projects related to renewable energy, damaged trees occur, assessment status and used method of evaluating the carbon storage of damaged trees were summarized. And after re-calculating the carbon storage of damaged trees through allometric equations, the difference between the two groups, re-calculated the damaged trees carbon storage and the damaged trees carbon storage in the report, was validated. As a result, damaged trees carbon storage in words was more than the re-calculated damaged trees carbon storage, and it was statistically significant (p<0.005). This result means that the existing method for calculating damaged tree carbon storage is overcalculated. It was judged that it was necessary to improve the calculation method. Therefore, allometric equations suitable for each dominated-tree species should be used when calculating the damaged tree carbon storage. Furthermore, we propose to establish a carbon storage calculation system based on actual data from the ecosystem so that researchers can efficiently and accurately the damaged trees carbon storage.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.275-284
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• 2023

Virtual reality simulations are used for education and training in various fields, and are especially widely used in the medical field recently. The education/training simulator consists of tactile/force feedback generation and image/sound output hardware that provides a sense similar to a doctor's treatment of a real patient using real surgical tools, and software that produces realistic images and tactile feedback. Existing simulators are complicated and expensive because they have to use various types of hardware to simulate various surgical instruments used during surgery. In this paper, we propose a dental surgical simulation system using a force feedback device and a morphable haptic controller. Haptic hardware determines whether the surgical tool collides with the surgical site and provides a sense of resistance and vibration. In particular, haptic controllers that can be deformed, such as length changes and bending, can express various senses felt depending on the shape of various surgical tools. When the user manipulates the haptic feedback device, events such as movement of the haptic feedback device or button clicks are delivered to the simulation system, resulting in interaction between dental surgical tools and oral internal models, and thus haptic feedback is delivered to the haptic feedback device. Using these basic techniques, we provide a realistic training experience of impacted wisdom tooth extraction surgery, a representative dental surgery technique, in a virtual environment represented by sophisticated three-dimensional models.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.1-19
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• 2023

Despite the growing importance of video-based social media content, such as vlogs, as a marketing tool in the travel industry, there is limited research on the characteristics that enhance engagement among potential travelers. This study explores the influence of emotional valence in YouTube travel content on viewer engagement, specifically likes and comments. We analyzed 4,619 travel-related YouTube videos from eight popular tourist cities. Using negative binomial regression analysis, we found that both positive and negative emotions significantly influence the number of likes received. Videos with higher positive emotions as well as negative emotions receive more likes. However, when it comes to the number of comments, only negative emotions showed a significant positive influence, while positive emotions had no significant impact. These findings offer valuable insights for marketers seeking to optimize engagement strategies on YouTube, considering the unique nature of travel products. Further research into the effects of specific emotions on engagement is warranted to improve marketing strategies. This study highlights the powerful impact of emotions on viewer engagement in the context of social media, particularly on YouTube.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.412-421
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• 2023

Ultrasound brain stimulation is spot-lighted by its capability of inducing brain cell activation in a localized deep brain region and ultimately treating impaired brain function while the efficiency and directivity of neural modulation are highly dependent on types of stimulus waveforms. Therefore, to optimize the types of stimulation parameters, we propose a cell-cultivable ultrasonic transducer having a series stack of a spin-coated polymer piezoelectric element (Poly-vinylidene fluoride-trifluorethylene, PVDF-TrFE) and a parylene insulating layer enhancing output acoustic pressure on a glass-coverslip which is commonly used in culturing cells. Due to the uniformity and high accuracy of stimulus waveform, tens of neuronal cell responses located on the transducer surface can be recorded simultaneously with fluorescence microscopy. By averaging the cell response traces from tens of cells, small changes to the low intensity ultrasound stimulations can be identified. In addition, the reduction of stimulus distortions made by standing wave generated from reflections between the transducers and other strong reflectors can be achieved by placing acoustic absorbers. Through the proposed ultrasound transducer, we could successfully observe the calcium responses induced by low-intensity ultrasound stimulation of 6 MHz, 0.2 MPa in astrocytes cultured on the transducer surface.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.37-55
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• 2023

With the development of electronic journals and the emergence of various interdisciplinary studies, the selection of journals for publication has become a new challenge for researchers. Even if a paper is of high quality, it may face rejection due to a mismatch between the paper's topic and the scope of the journal. While research on assisting researchers in journal selection has been actively conducted in English, the same cannot be said for Korean journals. In this study, we propose a system that recommends Korean journals for submission. Firstly, we utilize SBERT (Sentence BERT) to embed abstracts of previously published papers at the document level, compare the similarity between new documents and published papers, and recommend journals accordingly. Next, the order of recommended journals is determined by considering the similarity of abstracts, keywords, and title. Subsequently, journals that are similar to the top recommended journal from previous stage are added by using a dictionary of words constructed for each journal, thereby enhancing recommendation diversity. The recommendation system, built using this approach, achieved a Top-10 accuracy level of 76.6%, and the validity of the recommendation results was confirmed through user feedback. Furthermore, it was found that each step of the proposed framework contributes to improving recommendation accuracy. This study provides a new approach to recommending academic journals in the Korean language, which has not been actively studied before, and it has also practical implications as the proposed framework can be easily applied to services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.295-303
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• 2023

As the cloud computing market grows, a variety of cloud services are now reliably delivered. Administrative agencies and public institutions of South Korea are transferring all their information systems to cloud systems. It is essential to develop security solutions in advance in order to safely operate cloud services, as protecting cloud services from misuse and malicious access by insiders and outsiders over the Internet is challenging. In this paper, we propose a zero trust model for cloud storage services that store sensitive data. We then verify the effectiveness of the proposed model by operating a cloud storage service. Memory, web, and network forensics are also performed to track access and usage of cloud users depending on the adoption of the zero trust model. As a cloud storage service, we use Amazon S3(Simple Storage Service) and deploy zero trust techniques such as access control lists and key management systems. In order to consider the different types of access to S3, furthermore, we generate service requests inside and outside AWS(Amazon Web Services) and then analyze the results of the zero trust techniques depending on the location of the service request.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.