• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.5
• /
• pp.1009-1017
• /
• 2022

Log data has been used as a basis in understanding and deciding the main functions and state of information systems. It has also been used as an important input for the various applications in cybersecurity. It is an essential part to get necessary information from log data, to make a decision with the information, and to take a suitable countermeasure according to the information for protecting and operating systems in stability and reliability, but due to the explosive increase of various types and amounts of log, it is quite challenging to effectively and efficiently deal with the problem using existing tools. Therefore, this study has suggested a multiclass classification of the security severity level of multi-source event log using machine learning based on natural language processing. The experimental results with the training and test samples of 472,972 show that our approach has archived the accuracy of 99.59%.

• Journal of the Korean Institute of Gas
• /
• v.22 no.4
• /
• pp.13-26
• /
• 2018

Currently, local regulations, such as KS Code, do not clearly specify how to calculate the range of hazardous area, so the dispersion modeling program should be used to select dispersion. The purpose of this study is to present a methodology of determining the range of hazardous area which is simpler and more reasonable than modelling by using representative materials and process conditions. Based on domestic and overseas regulations that are currently in effect, variables affecting distance to LFL(Lower Flammable Limit) were selected. A total of 16 flammable substances were modelled for substance variables, process conditions variables, and weather conditions variables, and the statistical analysis selected the variables that affect them. Using the selected variables, a three-step classification method was prepared to select the range of locations subject to explosion hazard.

• Journal of the Korean Institute of Gas
• /
• v.26 no.6
• /
• pp.65-75
• /
• 2022

Recently, the possibility of fire and explosion due to hydrogen leakage and the resulting risk are increasing since the operating pressure of the electrolysis increases. This study performed the hazardous area classification in accordance with KS C IEC 60079-10-1 and KGS GC101 in consideration of the general operating conditions of the electrolysis. In addition, in order to achieve a To Non-hazardous, an appropriate ventilation rate was estimated to maintain a concentration of less than 25 % of the lower explosive limit. As a result, it was reviewed that the electrolysis is classified as an hazardous area when only natural ventilation is applied, and a huge amount of ventilation is required to classify it as a non-hazardous area.

• Journal of the Korean Institute of Gas
• /
• v.10 no.1 s.30
• /
• pp.19-25
• /
• 2006

Explosion limit is one of the major physical properties used to determine the fire and explosion hazards of the flammable substances. Explosion limits are used to classify flammable materials according to their relative flammability. Such a classification is important for the safe handling, storage, transportation of flammable substances. In this study, the lower explosion limits(LEL) of the flammable mixtures predicted with the appropriate use of the vapor composition and the heat of combustion of the individual components which constitute mixture. The values calculated by the proposed equations were a good agreement with literature data within a few percent. From a given results, It is to be hoped that this methodology will contribute to the estimation of the explosive properties of flammable mixtures with improved accuracy and the broader application for other flammable substances.

• Geomechanics and Engineering
• /
• v.9 no.3
• /
• pp.329-344
• /
• 2015

Cratering tests in rock are generally carried out to identify its fragmentation characteristics. The test results can be used to estimate the minimum amount of explosives required for the target volume of rock fragmentation. However, it is not easy to perform this type of test due to its high cost and difficulty in securing the test site with the same ground conditions as the site where blasting is to be performed. Consequently, this study investigates the characteristics of rock fragmentation by using the hydrocode in the platform of AUTODYN. The effectiveness of the numerical models adopted are validated against several cratering test results available in the literature, and the effects of rock mass classification and ground formation on crater size are examined. The numerical analysis shows that the dimension of a crater is increased with a decrease in rock quality, and the formation of a crater is highly dependent on a rock of lowest quality in the case of mixed ground. It is expected that the results of the present study can also be applied to the estimation of the level and extent of the damage induced by blasting in concrete structures.

• Journal of the Society of Disaster Information
• /
• v.11 no.3
• /
• pp.400-405
• /
• 2015

Operating system of CBRNE(Chemical, Biological, Radiological, Nuclear, and High-yield explosive)) weapon in North Korea has reached an internationally significant level. In preparation against the CBRNE weapon attack, the US is securing various forms of defense shelters and operating it based on classification by disaster characteristics. However, it is currently difficult to expect an efficient protective ability from South Korea due to the reckless designation of defense shelters without consideration of disaster characteristics. At this, this study examined the present condition of formerly used facilities, analyzed the characteristics of each facilities for the sorting of defense shelters that are possible of conversion into shelter against CBRNE weapon, and presented results and proposals gained through this study research.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.34 no.7
• /
• pp.47-57
• /
• 2018

As a preliminary step for developing vulnerability assessment model of terrorism, this study aims to deduce and classify architectural design elements of multi-use buildings to protect them from terrorism using explosives. For these objectives, eleven domestic and foreign guidelines of anti-terrorism, including RVS which is one of the commonly used tools for assessing vulnerability to terrorism, were analyzed. As results, 2 scenarios of explosive attack, 4 layers of defense, and 58 architectural design elements for risk assessment of terrorism were deduced. And the design elements were categorized into 18 groups based on their purpose and function to take into account the supplementary effects among them. Then, the design measures applicable for each element were classified into several grades on the basis of its protection or risk level. Lastly, 11 multi-use buildings were selected and investigated how the elements suggested in this study were applied to them.

• Journal of the Korea Convergence Society
• /
• v.12 no.7
• /
• pp.45-51
• /
• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• Journal of the Korean Institute of Gas
• /
• v.25 no.1
• /
• pp.40-47
• /
• 2021

In this study, hazards of decomposition and explosion for tert-butylperoxymaleate(TBPM), an organic peroxide, were evaluated by using various equipment to determine the cause of a fire explosion accident. As a result of DSC analysis, the instantaneous power density of TBPM was 26,401 kW/ml, and the NFPA reactive index(Nr) was classified as 4. And the positive value of EP(explosive propagation) and SS(shock sensitivity) showed that the TBPM had a potential hazard of explosion. From the experimental results, the shock sensitivity and friction sensitivity was rated as class 4 and 5, respectively. In the pressure vessel test, TBPM was ranked USA-PVT No.4 and evaluated as a self-reactive substance. In the combustion rate test, TBPM had the combustion rate of 167 mm/sec and was evaluated as the flammable solid classification 2 in GHS.

• The Korean Journal of Applied Statistics
• /
• v.35 no.1
• /
• pp.19-33
• /
• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.