• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.15-26
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• 2021

Recently, as the introduction of cloud, mobile, and IoT has become active, there is a growing need for technology development that can supplement the limitations of traditional security solutions based on fixed perimeters such as firewalls and Network Access Control (NAC). In response to this, SDP (Software Defined Perimeter) has recently emerged as a new base technology. Unlike existing security technologies, SDP can sets security boundaries (install Gateway S/W) regardless of the location of the protected resources (servers, IoT gateways, etc.) and neutralize most of the network-based hacking attacks that are becoming increasingly sofiscated. In particular, SDP is regarded as a security technology suitable for the cloud and IoT fields. In this study, a new access control system was proposed by combining SDP and hash tree-based large-scale data high-speed signature technology. Through the process authentication function using large-scale data high-speed signature technology, it prevents the threat of unknown malware intruding into the endpoint in advance, and implements a kernel-level security technology that makes it impossible for user-level attacks during the backup and recovery of major data. As a result, endpoint security, which is a weak part of SDP, has been strengthened. The proposed system was developed as a prototype, and the performance test was completed through a test of an authorized testing agency (TTA V&V Test). The SDP-based access control solution is a technology with high potential that can be used in smart car security.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.103-110
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• 2022

This study is a paper on the recognition of college students about the long-take shot technique which is often used to give a sense of reality and realism to films. 13 surveys were conducted on 92 students, including their perception of long-take films, their feelings after watching the film, satisfaction, and future prospects. Participants in the surveys consisted of 23 students in the health field, 16 students in the natural field, 41 students in the arts and sports field, and 13 students in the engineering field. As a result of the surveys, 68.8% of students answered "I know" about long-take film, and the feeling after watching the long-take film was found in the order of reality (realism) 68.8%, tension 16.1%, and boredom 15.1%. After watching the long-take film, 16.1% of students chose "Very satisfied" and 31.2% chose "Satisfied". Future prospects for long-take film showed high with 17.2% for "It will be developed very much" and 48.4% for "It will be developed". Preference for long-take film and general film was 67.7% for "long-take film" and 32.3% for "general film", showing high preference for long-take film. As a further research project, more in-depth surveys will be conducted targeting broadcasting & media contents majors in their 20s, and the long-take films used according to the story development process in domestic films will be analyzed.

• Quality Improvement in Health Care
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• v.28 no.1
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• pp.45-54
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• 2022

Purpose: The study aims to identify the theme list and priority criteria of patient safety theme reports in South Korea. Methods: The survey was conducted twice, and the importance of each criterion and theme was measured on a nine-point scale using the Delphi technique by a panel of 19 patient safety experts. The criteria included severity, universality, preventability, and organizational-social impact. Descriptive statistics such as frequency, percentage, mean, standard deviation, median, and interval quartile range were used to analyze the data. Results: The parameters were assigned a weighted average of 35% for severity, 20% for universality, 30% for preventability, and 15% for organizational-social impact, respectively. The final top three rankings were surgery safety, blood transfusion safety, and medication safety. In addition to expert opinion, for the theme that is selected based on the priority ranking, one to five sub-topics can be derived from the theme based on the priority ranking, societal demands, or the yearly priority list of patient safety incidents. Conclusion: It is recommended that the official patient safety center distribute the report in the form of a summary that can be utilized nationwide at medical institutions, government institutions, and other places. Updates, as well as accumulated theme reports, will serve as the baseline data for the proposal of the system and for the policy designed to implement and improve institutions' safety practices as a standard of domestic patient safety practice guidelines.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.67-74
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• 2022

In recent, the number of real-time traffic information sources and providers has increased as increasing smartphone users and intelligent transportation system facilities installed at roadways including vehicle detection system (VDS), dedicated short-ranged communications (DSRC), and global positioning system (GPS) probe vehicle. The accuracy of such traffic information would vary with these heterogeneous information sources or spatiotemporal traffic conditions. Therefore, the purpose of this study is to propose an empirical strategy of heterogeneous information fusion to improve the accuracy of real-time traffic information. To carry out this purpose, travel speed data collection based on the floating car technique was conducted on 227 freeway links (or 892.2 km long) and 2,074 national highway links (or 937.0 km long). The average travel speed for 5 probe vehicles on a specific time period and a link was used as a ground truth measure to evaluate the accuracy of real-time heterogeneous traffic information for that time period and that link. From the statistical tests, it was found that the proposed fusion strategy improves the accuracy of real-time traffic information.

• Explosives and Blasting
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• v.40 no.3
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• pp.19-32
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• 2022

Hydrogen is a fuel having the highest energy compared with other common fuels. This means hydrogen is a clean energy source for the future. However, using hydrogen as a fuel has implication regarding carrier and storage issues, as hydrogen is highly inflammable and unstable gas susceptible to explosion. Explosions resulting from hydrogen-air mixtures have already been encountered and well documented in research experiments. However, there are still large gaps in this research field as the use of numerical tools and field experiments are required to fully understand the safety measures necessary to prevent hydrogen explosions. The purpose of this present study is to develop and simulate 3D numerical modelling of an existing hydrogen gas station in Jeonju by using handheld LiDAR and Ansys AUTODYN, as well as the processing of point cloud scans and use of cloud dataset to develop FEM 3D meshed model for the numerical simulation to predict peak-over pressures. The results show that the Lidar scanning technique combined with the ANSYS AUTODYN can help to determine the safety distance and as well as construct, simulate and predict the peak over-pressures for hydrogen refueling station explosions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.613-621
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• 2022

Due to the global climate change, the rainfall volume and frequency on the Korean Peninsula are predicted to increase at the end of the 21st century. In addition, impervious surface areas have increased due to rapid urbanization which has caused the urban water cycle to deteriorate. Green Infrastructure (GI) researches have been conducted to improve the water cycle soundness; the efficiency of this technique has been verified through various studies. However, there are still no suitable GI design guidelines for this aspect. Therefore, the rainfall scenarios are set up for each percentile (60, 70, 80, 90) based on the volume and frequency analysis using 10-year rainfall data (Busan Meteorological Station). After determining the GI areas for each scenario, the runoff reduction characteristics are analyzed based on Storm Water Management Model (SWMM) 10-year rainfall-runoff-simulations. The total runoff reduction efficiency for each GI areas are computed to have a range of 13.1~52.1%. As a results of the quantitative analysis, the design rainfall for GI is classified into the 80~85 percentile in the study site.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.333-342
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• 2022

This paper has theoretically and practically reviewed the ISO (International Standard Organization) 17123-8 standard not only to raise the appropriateness for introducing performance criteria of GNSS (Global Navigation Satellite Systems) surveying equipment based on RTK (Real-Time Kinematic) accuracy but also to derive its proper test procedure by introducing the international standard. Field experiments have been performed to appreciate the GNSS-RTK accuracy of five selected receivers via the full testing procedure of the ISO standard, which statistically compares the so-called experimental standard deviations with themselves and with the reference accuracy. A series of statistical tests have revealed that the RTK accuracy of the same class receivers is not identical, whereas that of the different classes can be equivalent. Such a result evidences the urgency of adopting an RTK accuracy-based specification of the GNSS equipment to the performance standard, currently referenced to the static observation technique only. It is believed that this transition helps the maximization of a new generation of cost-effective receivers to legal surveying applications. Finally, this study proposes the ISO full test, comparing an experimental standard deviation with its referenced value, for a potential field verification procedure of the new performance standard.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.