• Knowledge Management Research
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• v.24 no.3
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• pp.73-94
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• 2023

With the virtual asset market's rapid growth, government regulations on listing and trading procedures are expected. However, specific measures are currently lacking. To ensure stable inclusion in the institutional framework, precise regulations are needed for market development and investor protection. This study compares self-regulatory guidelines of the top domestic virtual asset exchanges with Korea Exchange's Preliminary Listing Examination Standards (2022) to enhance timeliness and relevance. It defines IEO, IPO, and ICO concepts and addresses conflicts of interest in IEO. Analyzing delisted virtual assets, it categorizes issues and classifies listing examination guidelines into formal and qualitative requirements. The study examines self-regulatory guidelines based on continuity, transparency, stability, corporate characteristics, and investor protection criteria, along with five special requirements for virtual assets. Improvement measures include regular disclosures of governance structure, circulation volume, and the establishment of independent audit institutions. This research further analyzes delisting cases, classifies issues, and proposes solutions. Considering stock market similarities, it offers measures based on the institutional framework.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.363-376
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• 2023

According to the COVID-19, development of various medical software based on IoT(Internet of Things) was accelerated. Especially, interest in a central software system that can remotely monitor and control ventilators is increasing to solve problems related to the continuous increase in severe COVID-19 patients. Since medical device software is closely related to human life, this study aims to develop central monitoring system that can remotely monitor and control multiple ventilators in compliance with medical device software development standards and to verify performance of system. In addition, to ensure the safety and reliability of this central monitoring system, this study also specifies risk management requirements that can identify hazardous situations and evaluate potential hazards and confirms the implementation of cybersecurity to protect against potential cyber threats, which can have serious consequences for patient safety. As a result, we obtained medical device software manufacturing certificates from MFDS(Ministry of Food and Drug Safety) through technical documents about performance verification, risk management and cybersecurity application.The purpose of this study is to conduct a usability assessment to ensure that ergonomic design has been applied so that the ventilator central monitoring system can improve user satisfaction, efficiency, and safety. The rapid spread of COVID-19, which began in 2019, caused significant damage global medical system. In this situation, the need for a system to monitor multiple patients with ventilators was highlighted as a solution for various problems. Since medical device software is closely related to human life, ensuring their safety and satisfaction is important before their actual deployment in the field. In this study, a total of 21 participants consisting of respiratory staffs conducted usability test according to the use scenarios in the simulated use environment. Nine use scenarios were conducted to derive an average task success rate and opinions on user interface were collected through five-point Likert scale satisfaction evaluation and questionnaire. Participants conducted a total of nine use scenario tasks with an average success rate of 93% and five-point Likert scale satisfaction survey showed a high satisfaction result of 4.7 points on average. Users evaluated that the device would be useful for effectively managing multiple patients with ventilators. However, improvements are required for interfaces associated with task that do not exceed the threshold for task success rate. In addition, even medical devices with sufficient safety and efficiency cannot guarantee absolute safety, so it is suggested to continuously evaluate user feedback even after introducing them to the actual site.

• Composites Research
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• v.36 no.6
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• pp.441-447
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• 2023

Recently, as demand for high-strength, lightweight materials has increased, there has been great interest in joining with metals. In the case of mechanical bonding, such as bolting and riveting, chemical bonding using adhesives is attracting attention as stress concentration, cracks, and peeling occur. In this paper, surface treatment was performed to improve the adhesive strength, and the change in adhesive strength was analyzed. For the adhesive strength test were conducted with Carbon Fiber Reinforced Plastic(CFRP), CR340(Steel), and Al6061(Aluminum), and laser and plasma surface treatment were used. After plasma surface treatment, the adhesive strength improved by 7.3% and 39.2% in CFRP-CR340 and CFRP-Al6061, respectively. CR340-Al6061 was improved by 56.2% in laser surface treatment. Surface free energy(SFE) was measured by contact angle after plasma treatment, and it is thought that the adhesion strength was improved by minimizing damage through a chemical reaction mechanism. For laser surface treatment, it is thought that creates a rough bonding surface and improves adhesive strength due to the mechanical interlocking effect. Therefore, surface treatment is effect to improve adhesive strength, and based on this paper, the long-term fatigue test will be conducted to prevent fatigue failure, which is a representative cause of actual structural damage.

• Journal of Sensor Science and Technology
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• v.33 no.1
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• pp.34-39
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• 2024

Energy harvesting technology that converts the wasted energy resources into electrical energy is emerging as a semipermanent power source for self-powered electronics and wireless low-power sensor systems. Among the various energy conversion techniques, flexible piezoelectric energy harvesters (f-PEHs), using materials with piezoelectric effects, have attracted significant interest because they can harvest a small mechanical energy into electrical signals without constraints of time and space in various environments. In this study, we used a flexible piezoelectric composite film fabricated by dispersing BaHf_xTi_(1-x)O₃ (x = 0, 0.01, 0.05, 0.1) piezoelectric powders inside a polymeric matrix to facilitate f-PEHs. The fabricated f-PEH with optimal Hf contents (x = 0.05) generated a maximum output voltage of 0.95 V and current signal of 130 nA with stable electrical/mechanical disabilities under periodically bending deformations. In addition, we demonstrated a cantilever-type f-PEH and investigated its potential as a sensor by characterizing the output performance under mechanical vibrations at various frequencies. This study provides the breakthrough for realizing self-powered energy harvesting and sensing systems by adopting the lead-free piezoelectric composites under vibrational environments.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.4
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• pp.375-390
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• 2023

This study examined the genesis process and competitiveness determinants of overseas bio-industrial clusters. The bio industry is a promising new industry that major countries around the world are paying attention to because it can be applied to various industries and can create high added value by combining artificial intelligence and information and communication technology. In addition, the importance of clusters is emphasized in that it requires connection and cooperation with various stakeholders. However, compared to this importance and interest, related research in Korea is somewhat insufficient. In particular, overseas case studies are also overly biased toward a few leading clusters, and tend to produce policies and development plans that do not correspond to domestic local conditions. To alleviate this problem, this study looked at the birth and growth process of the BioHealth Capital Region in the United States, Cambridge Cluster in the United Kingdom, and Medicon Valley in Denmark and Sweden. Through this, we aim to enrich related case studies that were lacking, identify the determinants of competitiveness of each cluster, and present implications for the creation and development of domestic bio industry clusters.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.217-225
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• 2024

To foster the core talents of the future, the development of diverse and substantial SW·AI education programs is required, and a systematic system that can assist public education in SW and AI must be established. In this study, we develop and combine SW·AI education modules to construct a SW and AI education program applicable to public education. We also establish a systematic education system and provide sustainable SW·AI education to elementary, middle, and high school students through 'Job's Garage Camp' based on various sharing platforms. By creating a sustainable follow-up educational environment, students are encouraged to continue their self-directed learning of SW and AI. As a result of conducting a pre-post survey of students participating in the 'Job's Garage Camp', the post-survey values improved compared to the pre-survey values in all areas of 'interest', 'understanding and confidence', and 'career aspirations'. Based on these results, it can be confirmed that students had a universal positive perception and influence on SW and AI. Therefore, if the operation case of 'Job's Garage Camp' is improved and expanded, it can be presented as a standard model applicable to other SW and AI education programs in the future.

• Information Systems Review
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• v.21 no.2
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• pp.117-137
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• 2019

In the Big Data era, data science has become popular with the production of numerous data in various domains, and the power of data has become a competitive power. There is a growing interest in unstructured data, which accounts for more than 80% of the world's data. Along with the everyday use of social media, most of the unstructured data is in the form of text data and plays an important role in various areas such as marketing, finance, and distribution. However, text mining using social media is difficult to access and difficult to use compared to data mining using numerical data. Thus, this study aims to develop Korean Natural Language Application (KoALA) as an integrated application for easy and handy social media text mining without relying on programming language or high-level hardware or solution. KoALA is a specialized application for social media text mining. It is an integrated application that can analyze both Korean and English. KoALA handles the entire process from data collection to preprocessing, analysis and visualization. This paper describes the process of designing, implementing, and applying KoALA applications using the design science methodology. Lastly, we will discuss practical use of KoALA through a block-chain business case. Through this paper, we hope to popularize social media text mining and utilize it for practical and academic use in various domains.

• Journal of Ginseng Culture
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• v.6
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• pp.80-104
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• 2024

Ginseng, a representative medicinal plant of South Korea, is also highly valued in North Korea. However, due to limited access to information about North Korea, the actual cultivation, research and development trends, and related industry status of ginseng in North Korea are not well known. In this study, we aimed to understand the current status and research trends of the ginseng industry in North Korea based on limited available literature. In the North Korean pharmacopoeia, ginseng is referred to as "Koryo ginseng" and is defined as the roots of 6-year-old ginseng cultivated in the Kaesong region. The pharmacopoeia includes 22 types of ginseng preparations. In addition, 10 ginseng preparations are included in North Korea's Essential Drug List, and various health supplements, cosmetics, and toothpastes containing ginseng have been developed, distributed, and sold. Since 2014, the ginseng industry and research in North Korea have become more active overall. During this period, the ginseng cultivation area in Kaesong has been significantly expanded, and the facilities have been renovated. The Kaesong Koryo Ginseng Processing Plant has been equipped with sterilized, modernized facilities since 2016 and has been in operation. Since 2017, there has been a growing interest in quality control research, leading to the introduction of quality management regulations and certification systems in 2019. In the 1990s, there was significant research on ginseng product development, and since the 2000s, studies on the pharmacological effects and clinical research of ginseng have been reported. Additionally, research on ginseng cultivation and ginseng processing industries to increase yield has been emphasized. Ginseng, as a representative medicinal crop of Korea, holds great importance for both South and North Korea. Given its significance and the potential for synergy through mutual cooperation, ginseng serves as an ideal subject for inter-Korean exchange and collaboration.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4447-4464
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• 2023

This paper focuses on the development of a new computational model of the CNESTEN's TRIGA Mark II research reactor using the 3D continuous energy Monte-Carlo code TRIPOLI-4 (T4). This new model was developed to assess neutronic simulations and determine quantities of interest such as kinetic parameters of the reactor, control rods worth, power peaking factors and neutron flux distributions. This model is also a key tool used to accurately design new experiments in the TRIGA reactor, to analyze these experiments and to carry out sensitivity and uncertainty studies. The geometry and materials data, as part of the MCNP reference model, were used to build the T4 model. In this regard, the differences between the two models are mainly due to mathematical approaches of both codes. Indeed, the study presented in this article is divided into two parts: the first part deals with the development and the validation of the T4 model. The results obtained with the T4 model were compared to the existing MCNP reference model and to the experimental results from the Final Safety Analysis Report (FSAR). Different core configurations were investigated via simulations to test the computational model reliability in predicting the physical parameters of the reactor. As a fairly good agreement among the results was deduced, it seems reasonable to assume that the T4 model can accurately reproduce the MCNP calculated values. The second part of this study is devoted to the sensitivity and uncertainty (S/U) studies that were carried out to quantify the nuclear data uncertainty in the multiplication factor k_eff. For that purpose, the T4 model was used to calculate the sensitivity profiles of the k_eff to the nuclear data. The integrated-sensitivities were compared to the results obtained from the previous works that were carried out with MCNP and SCALE-6.2 simulation tools and differences of less than 5% were obtained for most of these quantities except for the C-graphite sensitivities. Moreover, the nuclear data uncertainties in the k_eff were derived using the COMAC-V2.1 covariance matrices library and the calculated sensitivities. The results have shown that the total nuclear data uncertainty in the k_eff is around 585 pcm using the COMAC-V2.1. This study also demonstrates that the contribution of zirconium isotopes to the nuclear data uncertainty in the k_eff is not negligible and should be taken into account when performing S/U analysis.

• Journal of IKEEE
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• v.28 no.3
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• pp.474-478
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• 2024

In this paper, we proposes a deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers. The proposed deep learning network is specifically designed for pharmaceutical containers by using data produced in real manufacturing environments, leading to more accurate quality inspection. Additionally, the use of an inline-capable deep learning network allows for an increase in inspection speed. The development of the deep learning network for quality inspection in the multi-camera inline inspection system consists of three steps. First, a dataset of approximately 10,000 images is constructed from the production site using one line camera for foreign substance inspection and three area cameras for dimensional inspection. Second, the pharmaceutical container data is preprocessed by designating regions of interest (ROI) in areas where defects are likely to occur, tailored for foreign substance and dimensional inspections. Third, the preprocessed data is used to train the deep learning network. The network improves inference speed by reducing the number of channels and eliminating the use of linear layers, while accuracy is enhanced by applying PReLU and residual learning. This results in the creation of four deep learning modules tailored to the dataset built from the four cameras. The performance of the proposed deep learning network for quality inspection in the multi-camera inline inspection system for pharmaceutical containers was evaluated through experiments conducted by a certified testing agency. The results show that the deep learning modules achieved a classification accuracy of 99.4%, exceeding the world-class level of 95%, and an average classification speed of 0.947 seconds, which is superior to the world-class level of 1 second. Therefore, the effectiveness of the proposed deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers has been demonstrated.