• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.8
• /
• pp.570-578
• /
• 2019

In this era of big data, a variety of government organizations are trying to create new added value via Information Integration. Therefore, several projects related to government agencies' information sharing have activated system connection/integration. The risk factors of system operation, however, have increased as the volume of Information Integration System grows. The interference in information sharing is predicted to affect the operation of the agencies, and the issue will grow even worse with massive impact on civil society when the agency operation is interrupted due to system failures in terms of infrastructure, software, data quality, and security. Diverse studies related to the maintenance of Information System have been conducted, but there is currently no evaluation framework for the operational system of Information Integration between various government agencies. In this respect, this study distinguishes each of the Information System components, Data, IT, People, Process, systematizes with Plan-Do-See, and finally presents a maturity model for Information Integration. Nine derived processes were analyzed through interview and questionnaires from Information Integration System officials, further suggesting maturity stage applying CMMI. This model allows diagnosis of the maturity level of an Information Integration System, and is expected to be utilized as resource for improving organizational processes.

• Journal of The Korean Society of Clinical Toxicology
• /
• v.19 no.1
• /
• pp.1-7
• /
• 2021

Purpose: The purpose of this study was to conduct a systematic review to investigate the socio-economic benefits of the poison control center (PCC) and to assess whether telephone counseling at the poison control center affects the frequency of emergency room visits, hospitalization, and length of stay of patients with acute poisoning. Methods: The authors conducted a medical literature search of the PubMed, EMBASE, and Cochrane Library databases. Two reviewers evaluated the abstracts for eligibility, extracted the data, and assessed the study quality using a standardized tool. Key results such as the cost-benefit ratio, hospital stay days, unnecessary emergency room visits or hospitalizations, and reduced hospital charges were extracted from the studies. When meta-analysis was possible, it was performed using RevMan software (RevMan version 5.4). Results: Among 299 non-duplicated studies, 19 were relevant to the study questions. The cost-benefit ratios of PCC showed a wide range from 0.76 to 36 (average 6.8) according to the level of the medical expense of each country and whether the study included intentional poisoning. PCC reduced unnecessary visits to healthcare facilities. PCC consultation shortened the length of hospital stay by 1.82 (95% CI, 1.07-2.57) days. Conclusion: The systematic review and meta-analysis support the hypothesis that the PCC operation is cost-beneficial. However, when implementing the PCC concept in Korea in the future, it is necessary to prepare an institutional framework to ensure a costeffective model.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.141-148
• /
• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.5
• /
• pp.249-257
• /
• 2022

This paper presents an improved computational framework for the direct-solution-based finite element tearing and interconnecting (FETI) algorithm. The FETI-local algorithm is further improved herein, and localized Lagrange multipliers are used to define the interface among its subdomains. Selective inverse entry computation, using a property of the Boolean matrix, is employed for the computation of the subdomain interface stiffness and load, in which the original FETI-local algorithm requires a full matrix inverse computation of a high computational cost. In the global interface computation step, the original serial computation is replaced by a parallel multi-frontal method. The performance of the improved FETI-local algorithm was evaluated using a numerical example with 64 million degrees of freedom (DOFs). The computational time was reduced by up to 97.8% compared to that of the original algorithm. In addition, further stable and improved scalability was obtained in terms of a speed-up indicator. Furthermore, a performance comparison was conducted to evaluate the differences between the proposed algorithm and commercial software ANSYS using a large-scale computation with 432 million DOFs. Although ANSYS is superior in terms of computational time, the proposed algorithm has an advantage in terms of the speed-up increase per processor increase.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.4
• /
• pp.817-839
• /
• 2021

To manage software safely, the military acquires and manages products in accordance with the RMF A&A. RMF A&A is standard for acquiring IT products used in the military. And it covers the requirements, acquisition through evaluation and maintenance of products. According to the RMF A&A, product development activities should reflect the risks of the military. In other words, developers have mitigated the risks through security by design and supply chain security. And they submit evidence proving that they have properly comply with RMF A&A's security requirements, and the military will evaluate the evidence to determine whether to acquire IT product. Previously, case study of RMF A&A have been already conducted. But it is difficult to apply in real-world, because it only address part of RMF A&A and detailed information is confidential. In this paper, we propose the evidence fulfilling method that can satisfy the requirements of the RMF A&A. Furthermore, we apply the proposed method to real-world drone system for verifying our method meets the RMF A&A.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.spc
• /
• pp.11-17
• /
• 2022

In this study, we address the development of a floating platform system based on a unmanned surface vessel for wide-area sensing and monitoring of hazardous and noxious substances (HNSs). For long endurance, a movable floating platform with no mooring lines was used and modified for HNS sensing and monitoring. The floating platform was equipped with various sensors such as optical and thermal imaging cameras, marine radar, and sensors for detecting HNSs in water and air. Additionally, for experiment validation in real outdoor environments, a portable gas-exposure system (PGS) was built and installed on the monitoring system. The software for carrying out the mission was integrated with the Robot Operating System (ROS) framework. The practical feasibility of the developed system was verified through experimental tests conducted in inland water and real-sea environments.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.9
• /
• pp.419-430
• /
• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.1
• /
• pp.355-360
• /
• 2023

Recently, with the development of digital, children are exposed to a lot of media media. Reading activity decreases, making it difficult to acquire information from books or organize and remember acquired information. Since education with augmented reality increases children's participation and immersion in learning, we proposed a reading activity application with augmented reality technology to increase children's reading interest and creativity. In addition, based on the five nurturing processes, we designed a play-oriented reading activity for the application. In addition to the application suggestions, we designed a play-centered lesson model so that it can be used in actual lessons. In order to analyze the conceptual thinking framework according to the lesson model design, we visited an actual daycare center and conducted a class attended by an expert. Experts who participated were asked to fill out a pre-produced questionnaire to review the suitability of the reading activity class model and operation, and the feasibility was examined. Our lesson model design was based on limited book content, and due to ethical concerns, large-scale experiments with children could not be conducted, so the results of the study are not representative of the whole. However, it is significant that the possibility of running a new reading activity class based on the Nuri course has been examined and proposed.

• Information Systems Review
• /
• v.22 no.2
• /
• pp.101-120
• /
• 2020

This study investigates the effects of privacy psychological characteristics of B2C logistics services users on their willingness to comply with their logistics companies' information protection policy. Using cognitive valence theory as a theoretical framework, this study proposes a research model to examine the relationships between users' logistics security knowledge, privacy trust, privacy risk, privacy concern, and their willingness of information protection policy compliance. To test the proposed model, we conducted a survey from actual users of logistics services and collected valid 151 samples. We analyzed the data using a structural equation modeling software. The empirical results show that logistics security knowledge positively affects privacy trust; privacy concern positively influences privacy risk; privacy trust, privacy risk, and privacy concern positively influence behavioral willingness of compliance. However, logistics security knowledge does not affect behavioral willingness of compliance. The results of the study provide several contributions to the literature of B2C logistics services domain and managerial implications to logistics services companies.

• Journal of the Korean Society of Radiology
• /
• v.83 no.6
• /
• pp.1298-1311
• /
• 2022

Purpose To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.