• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.190-196
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• 2020

FRACAS(Failure Reporting, Analysis and Corrective Action System) has been applied in various industries to improve the reliability of the systems. FRACAS is effective in improving reliability by repeating failure analysis, proper corrective action, and result verification for identified failures. However, FRACAS has many limitations in terms of process, data collection and management to be integrated into the existing development environment. In the domestic defense industry, studies on the development of FRACAS system and process improvement have been conducted to solve the difficulties of applying FRACAS, but most of them are concentrated in the operation/maintenance phase. Since FRACAS should be conducted in consideration of TLCSM(Total Life Cycle System Management), it is necessary to study the reference architecture so that FRACAS can be applied from the early design phase. In this paper, we studied the TLCSM-based integrated architecture considering the system life cycle phases, FRACAS closed-loop process, and FRACAS essentials in order to effectively apply FRACAS throughout the life cycle of defense systems. The proposed architecture was used as a reference model for FRACAS in a shipboard combat system.

• Journal of Korea Multimedia Society
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• v.11 no.7
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• pp.1007-1015
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• 2008

In order to provide real-time data from sensors and instruments at manufacturing processes on web, we proposed a communication service model based on XML(eXtensible Markup Language). HTML(Hyper Text Markup Language) is inadequate for describing real-time data from manufacturing plants while it is suitable for display of non-real-time multimedia data on web. For applying XML-based web service of process data in Intranet environment, real-time performance of communication services was evaluated to provide the system design criteria. XML schema for the data presentation was proposed and its communication performance was evaluated by simulation in terms of transmission delay due to increased message length and processing delay for transformation of raw data into defined format. For transformation of raw data into XML format, we proposed two structures: one is the scheme where transformation is done at an SCC(Supervisory Control Computer) after receiving real-time data from instruments. the other is the scheme where transformation is carried out at instruments before the data are transmitted to the SCC. Performances of two structures were evaluated on a testbed under various conditions such as six packet sizes and offered loads of 20%, 50% and 80%, respectively. Test results show that proposed schemes are applicable to the systems in Ethernet 100BaseT network if total message traffic is less than 7 Mbps.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.37-45
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• 2019

In recent years, the emergence of virtual reality (VR Virtual Reality) technology has provided a new model of safety education, enabling users to learn and respond to disasters in a virtual safety education environment. However, the related VR products related to domestic and foreign R & D are relatively simple, there is no practical training on specific accident, and it is not practical enough to play a sufficient role in safety education. In this paper, the problems and disadvantages of VR technology applied in the field of automobile safety education as an example of automobile accident among the types of disasters are examined, and a system framework of automotive safety education based on VR technology is proposed. The vehicle safety education system proposed in this paper will help users to improve driving safety consciousness, to acquire safety knowledge in driving, and to acquire driving safety skill which is very important for automobile safety education. In addition, the design and production methods of safety education based on VR technology are considered to have important reference implications for the application of modern teaching and teaching theory by integrating with VR technology and developing related teaching materials products and finally introducing education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.627-635
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• 2019

A programmable drone is a drone developed not only to experience the basic principles of flight but also to control drones through Arduino-based programming. Due to the nature of the training drones, the main users are students who are inexperienced in controlling the drones, which often cause frequent collisions with external objects, resulting in high damage to the drones' frame. In this study, the structural stability of the drone was evaluated by means of a structural dynamics based collision simulation for educational drone frame. Collision simulations were performed on three cases according to the impact angle of $0^{\circ}$, $+15^{\circ}$ and $-15^{\circ}$, using an analytical model with approximately 240,000 tetrahedron elements. Using ANSYS LS-DYNA, which provides excellent functions for the simulation of the dynamic behavior of three-dimensional structures, the stress distribution and strain generated on the drone upper, the drone lower, and the ring assembly were analyzed when the drones collided against the wall at a rate of 4 m/s. Safety factors resulting from the equivalent stress and the yield strain were calculated in the range of 0.72 to 2.64 and 1.72 to 26.67, respectively. To ensure structural stability for areas where stress exceeds yield strain and ultimate strain according to material properties, the design reinforcement is presented.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.127-136
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• 2019

Tunnel construction by TBMs should be supported by the performance of a screw conveyor in order to obtain the optimum penetration rate, so studies related to the screw conveyor performance have been being conducted. Compared to the study on the performance of the screw conveyor for the soil, however, the research on the performance of the screw conveyor for the rock is insufficient. Considering the domestic tunnel sites with more rock layers than soil layers, simulation of discharge of 6 types of rock chips by the screw conveyor was conducted using DEM. Regardless of the shape and volume of the rock chips, the discharge rates of the rock chips by the parallel placed screw conveyor at a speed of 10 RPM in the same rock mass were about 20% (standard deviation: 1.3%) of the maximum volume of discharge rate by the screw conveyor. It is expected that this study can be used as a reference material for screw conveyor design and operation in TBM excavations in rock masses.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.101-109
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• 2021

Wave observations using a marine X-band radar are conducted by analyzing the backscattered radar signal from sea surfaces. Wave parameters are extracted using Modulation Transfer Function obtained from 3D wave number and frequency spectra which are calculated by 3D FFT of time series of sea surface images (42 images per minute). The accuracy of estimation of the significant wave height is, therefore, critically dependent on the quality of radar images. Wave observations during Typhoon Maysak and Haishen in the summer of 2020 show large errors in the estimation of the significant wave heights. It is because of the deteriorated radar images due to raindrops falling on the sea surface. This paper presents the algorithm developed to increase the accuracy of wave heights estimation from radar images by adopting convolution neural network(CNN) which automatically classify radar images into rain and non-rain cases. Then, an algorithm for deriving the Hs is proposed by creating different ANN models and selectively applying them according to the rain or non-rain cases. The developed algorithm applied to heavy rain cases during typhoons and showed critically improved results.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.236-238
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• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.