• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1706-1712
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• 2022

Recently, the safety of chemical substances has gained attention due to incidents occurring in petrochemical industrial complexes, such as gas leaks and fires. In particular, industrial complexes in Ulsan and Yeosu (South Korea) are valuable as they significantly contribute to the petrochemical industry, but accidents may occur due to chemical leakage. Therefore, in this study, sensor nodes are configured at an interval of 20 [m] based on outdoor facilities standards to respond to chemical accidents, and exposure consideration of 8 h (TWA) and 15 min (STEL) are proposed in TLVs. The proposed system pre-processes data collected in multi-hop communication at a cycle of 0.6-0.75 [s] using Python and stores it in the MySQL database through SQL and a real-time remote monitoring system that updates the stored data once every 5 s is implemented by linking MySQL and Grafana.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.5
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• pp.367-374
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• 2023

As the Fourth Industrial Revolution accelerating, countries worldwide are developing technologies to digitize and automate various industrial sectors. Building smart factories not only reduces costs through improved process productivity but also allows for preemptive identification and removal of risk factors through the practice of Health, Safety, and Environment (HSE) management, thereby reducing industrial accident risks. In this study, we visualized pressure, temperature, power, and wind speed data measured in real-time via a monitoring GUI, enabling field managers and workers to easily access related information. Through the work environment monitoring system developed in this study, it is possible to conduct economic analysis on per-unit basis, based on the digitization of production management elements and the tracking of required resources. By implementing HSE in shipyards, potential risk factors can be improved, and gas and electrical leaks can be identified, which are expected to reduce production costs.

• Journal of The Korea Institute of Healthcare Architecture
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• v.30 no.2
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• pp.35-46
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• 2024

Purpose: It is the responsibility of public healthcare to respond quickly to infectious disease outbreaks and disasters such as MERS, COVID-19, the Syrian earthquake, and the Miryang Sejong Hospital fire accident. It is very important to secure safe medical facilities and protect lives through emergency medical support and disaster response systems. The purpose of this study is to investigate the safety status of regional medical facilities that play a central role in the event of a disaster. Methods: The target was 41 local public hospitals, including 35 regional medical centers and 6 Red Cross hospitals nationwide. We delivered a questionnaire to 41 medical facilities and collected data from 32 regional public hospitals that received responses. Results: In order to respond to safety accidents, a survey was conducted on infections, falls, patient identification, and incorrect connections for medical accidents, and for in-hospital accidents, a survey was conducted on entrapment, collision, water leaks, falling objects, and crime prevention. For natural disasters, we investigated the response environment for typhoons, floods, and snow damage, and for social disasters, we investigated the response environment for fire, power outages, and radiation damage. Implications: We hope that it will be used as basic data for developing standards and creating hospital facilities and environments that are safe for everyone to respond to various disasters and prevent patient safety accidents in the future.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.17-23
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• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1261-1270
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• 2022

The purpose of this study is to collect and analyze information related to water leaks that cannot be easily accessed, and utilized by using the news search results that people can easily access. We applied a web crawling technique for extracting big data news on water leakage accidents in the water supply system and presented an algorithm in a procedural way to obtain accurate leak accident news. In addition, a data analysis technique suitable for water leakage accident information analysis was developed so that additional information such as the date and time of occurrence, cause of occurrence, location of occurrence, damaged facilities, damage effect. The primary goal of value extraction through big data-based leak analysis proposed in this study is to extract a meaningful value through comparison with the existing waterworks statistical results. In addition, the proposed method can be used to effectively respond to consumers or determine the service level of water supply networks. In other words, the presentation of such analysis results suggests the need to inform the public of information such as accidents a little more, and can be used in conjunction to prepare a radio wave and response system that can quickly respond in case of an accident.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.39-43
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• 2017

Development of vision system as a nondestructive evaluation system can be very useful in screening the defective mechanical parts before they are assembled into the final product. Since the tens of thousands of the mechanical parts are used in an automobile carefully inspecting the quality of the mechanical parts is very important to maximize the performance of the automobile. To sort out the defective mechanical parts before they are assembled, auto parts fabrication companies employ various inspection systems. Nondestructive evaluation systems are getting rapidly popular among various inspection systems. In this study, we have developed a vision system to inspect the inside of the brake caliper, a part that is used to compose a brake which is the most important to the safety of the drivers and the passengers. In a brake caliper, a piston is pushed against the brake disk by oil pressure, causing a friction to damp the rotation of the wheel. Inside the caliper, a groove is positioned to adopt an oil seal to prevent the oil leaks. Inspecting the groove with our vision system, we could examine the existence of the contaminants which are normally the residual tiny pieces from the machining process. We used a high resolution GigE camera, 360 degree lens to look in the inside view of the caliper at once, and a special illumination system in this vision system. We used the edge detection technique to successfully detect the contaminants which were in the form of small metal chips. Labview graphical program was used to process the digital data from the camera and to display the vision and the statistics of the contaminants. We were very successful in detecting the contaminants from the various size calipers. We think we are ready to employ this vision system to the caliper production factories.

• Journal of the Korean Society of Groundwater Environment
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• v.4 no.3
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• pp.122-129
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• 1997

Spills or leaks of hazardous organic. compounds from underground storage tanks (USTs) are common contaminant sources of soil and groundwater. It would aid in managing USTs to assess and rank the potential environmental hazard posed by the USTs. Therefore, a preliminary hazard ranking system of USTs is developed in this study. The system is combined with GIS and consists of five steps: 1) selection of significant factors, 2) determination of the hierarchy of the factors. 3) determination of the weights, 4) calculation of the potential hazard, and 5) hazard assessment. The system is applied to the gas stations in Kwanak-gu, Seoul. The results indicate that the gas stations can be categorized in three groups as highly hazardous, less highly hazardous, and weakly hazardous. Seven gas stations belong to the highly hazardous group. Through the sensitivity analysis, four stations appear to possess high hazard potentials regardless of weights assigned to the factors. It appears that a user can make flexible application of the hazard ranking system with the user's experience and particular purposes. However, the system still needs validations against field survey data.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.1
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• pp.259-266
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• 1990

Leaks at the piston seal and the by-pass port of a small volume prover have relatively large influence on the proving accuracy in comparison with a conventional ball prover. The pulse interpolator, which is to increase the discrimination, is affected by the characteristic of the flowmeter signal. In this study, a small volume prover of the double cylinder type was designed in order to study the pulse interpolation error as well as the leak error. The basic volume of the prover determined by a water draw method was about 9.68L. Experimental results revealed that interpolation data attained by the repeated piston pass for turbine meters at a fixed flowrate may be treated effectively by applying a statistical method. It was possible to limit the pulse interpolation error less than .+-. 0.02% at the 95% confidence level. However, in the case of the bulk meter, if failed to achieve the required repeatability level because of the pulse characteristics. The basic volume change appeared to be independent of the piston velocity within the .+-. 0.05% of tolerance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.849-861
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• 2015

As the rise in popularity of online games, the users start exchanging rare items for real money. As RMT (Real Money Trade) is prevalent, GFG (Gold Farming Group) who abuse RMT shows up. GFG causes social problems such as identity theft, privacy leaks. Because they needs many bot characters to gather game items. In addition, GFG induce RMT that makes in-game problems such as a destroying game economy, account hacking. Therefore, It is very important work to collapse GFG at the perspective of social and in-game. In this paper, we proposed a fundamental method for detecting RMT buyers for the collapse of GFG at the perspective of buyer by Law of Demand and Supply. We found two type of RMT by analyzing actual game data and detected RMT buyers with high recall ratio of 98% by ruled-based detection.