• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1098-1105
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• 2021

The International Maritime Organization has steadily strengthened environmental regulations on nitrogen oxides and carbon dioxide emitted from marine vessels. Consequently, the demand for electric propulsion vessels based on eco-friendly elements has increased. To this end, research and development has been steadily conducted for various vessels. In electric propulsion systems, a redundancy configuration is typically adopted to increase reliability and facilitate the onboard arrangement. Furthermore, studies have been actively conducted to ensure the safety of electric propulsion systems through the combination with digital twin technology. A digital twin can be used to predict outcomes in advance by implementing real-world equipment or space in a virtual world like twins, integrating real-world information and data with the virtual world, and performing computer simulations of situations that can occur in a real environment. In this study, we perform failure modes and effects analysis (FMEA) to validate the electric power management system (PMS) redundancy scheme for the digital twin technology development of electric propulsion vessels. Then, we propose the role and algorithm of PMS as a compensation function for preventing primary and secondary damages caused by a single equipment failure of the PMS and preventing additional damages by analyzing the impact on the entire system under real vessel operating conditions based on the redundancy FMEA suggested for the ship classification and certification. We verified the improvement in propulsion conservation through tests.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.253-262
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• 2023

E-voting is a concept that includes actions such as kiosk voting at a designated place and internet voting at an unspecified place, and has emerged to alleviate the problem of consuming a lot of resources and costs when conducting offline voting. Using E-voting has many advantages over existing voting systems, such as increased efficiency in voting and ballot counting, reduced costs, increased voting rate, and reduced errors. However, centralized E-voting has not received attention in public elections and voting on corporate agendas because the results of voting cannot be trusted due to concerns about data forgery and modulation and hacking by others. In order to solve this problem, recently, by designing an E-voting system using blockchain, research has been actively conducted to supplement concepts lacking in existing E-voting, such as increasing the reliability of voting information and securing transparency. In this paper, we proposed an electronic voting system that introduced hybrid blockchain that uses public and private blockchains in convergence. A hybrid blockchain can solve the problem of slow transaction processing speed, expensive fee by using a private blockchain, and can supplement for the lack of transparency and data integrity of transactions through a public blockchain. In addition, the proposed system is implemented as BaaS to ensure the ease of type conversion and scalability of blockchain and to provide powerful computing power. BaaS is an abbreviation of Blockchain as a Service, which is one of the cloud computing technologies and means a service that provides a blockchain platform ans software through the internet. In this paper, in order to evaluate the feasibility, the proposed system and domestic and foreign electronic voting-related studies are compared and analyzed in terms of blockchain type, anonymity, verification process, smart contract, performance, and scalability.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.42-57
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• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• Journal of Industrial Convergence
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• v.21 no.3
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• pp.195-202
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• 2023

This study analyzes the results of a survey based on classes conducted in the field to understand the educational needs of learners, and reflects the elements necessary for SW education. In this study, various experimental elements according to learning motivation and learning achievement were constructed and designed through previous studies. As a survey applied to this study, experimental elements in three categories: Faculty Competences(FC), Learner Competences(LC), and Educational Conditions(EC) were analyzed by primary area and secondary major, respectively. As a result of analyzing CT-based SW education by area, the development of educational materials, understanding of lectures, and teaching methods showed high satisfaction, while communication with students, difficulty of lectures, and the number of students were relatively low. The results of the analysis by major were found to be more difficult and less interesting in the humanities than in the engineering field. In this study, Based on these statistical results proposes the need for non-major SW education to improve into an interesting curriculum for effective liberal arts education in the future in terms of enhancing learners' problem-solving skills.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.129-139
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• 2022

This study aims to identify which factors affect successful big data system construction, identify the relationship between the factors, and identify the success model and success factors necessary for public institutions to build big data systems. Therefore, the preceding and related studies related to this study were reviewed, and success factors for the establishment of a big data system were derived based on this. As a research method, a survey was conducted on users of institutions that have established or planned to build a big data system, and a structural equation (AMOS) was conducted to verify the impact relationship between success factors. As a result of the analysis, organizational support factors, development support factors, user support factors, information quality, service quality, system quality, use, and net benefit were derived as success factors for building big data systems, and a success model was presented. This can be seen as significant and academic contributions in that it is the first study of the success model for building an information system reflecting big data characteristics, and it is expected that this study will be used as basic data for building a big data system in public institutions in the future.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.27-33
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• 2023

In this study, it was analyzed with the dyeing pattern of Diospyros kaki Thunb (persimmon) and was tried to numerically evaluate how the dyeing pattern in silk fabrics and cells was changed by different mordants. When the dyed silk fabrics were sufficiently dried, the silk fabrics were found to have a pale yellow color. Interestingly, iron II sulfate mordant changed the color change the most, silk fabrics were dyed with a color close to brown or dark purple. For numerical analysis, 19% and 62.5% color changes could be induced by sodium tartrate plus citric acid and copper acetate, respectively. Iron II sulfate induced the greatest difference than that of untreated mordants at 88%. About 5% and 10% of Chinese hamster ovary (CHO) cells were stained by sodium tartrate plus citric acid and copper acetate, respectively. The staining effect induced by iron II sulfate was about 2.4 times higher than the staining effect by sodium tartrate plus citric acid. In previous studies, staining results have been visually confirmed. However, this results not only visually confirmed the dyeing, but also quantified the color change. In particular, if numerical results are continuously integrated into big data, any researcher will be able to easily obtain similar results even if the method, time, volume, etc. are changed. In addition, the numerical data of this study is considered to be an important basis for building a database for IoT construction and computer analysis.

• Journal of Service Research and Studies
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• v.11 no.1
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• pp.45-58
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• 2021

Requirements engineering can be seen as proceeding with various processes and activities such as extraction, analysis, specification, management, and verification without temporal and spatial constraints in the development environment of information systems that are becoming large and decentralized. Developing requirements well and conducting continuous evaluation and management is the shortcut to success in project management, and it is recognized as a very important matter in relation to requirements in the information system audit. When we conduct information system audit and conducting projects subject to audit, we need to improve the required engineering aspect. Therefore, this study derives inspection items suitable for the target project by referring to the audit inspection manual and audit inspection guide when conducting the current audit, and relates to the required engineering aspect among the contents of the inspection guide for each business type that is the basis for deriving the inspection items were derived for each audit point/audit area for the project management and quality assurance project type corresponding to the inspection items. The suitability of the extracted occupation items was verified through a questionnaire survey by experts.