• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.844-850
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• 2008

This paper stated the proposed algorithm to certify the characteristics of artificial explosion sounds traveled through underground bedrock medium. Artificial explosion that travel through underground bedrock had an attenuation within high frequency bands in increase of a distance with multiple transmission paths phenomenon and inhomogeneity of geological status. In this paper, explosion experiment was made in underground tunnel to verify efficiency of proposed algorithm. The could certify the characteristics of artificial explosion sounds as extracted and numerically quantified the characterized parameter with collected sound sample that traveled through underground bedrock channel.

• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.3
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• pp.205-209
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• 2003

Solubilization of domestic household waste through Steam explosion with Subsequent ethanol production by the microbial saccharifitation and fermentation of the exploded product was studied. The effects of steam explosion on the changes of the density, viscosity, pH, and amounts of extractive components in artificial household waste were determined. The composition of artificial waste used was similar to leftover waste discharged from a typical home in Japan. Consecutive microbial saccharification and fermentation, and simultaneous microbial saccharification and fermentation of the Steam-exploded product were attempted using Aspergillus awamori, Trichoderma viride, and Saccharomyces cerevisiae; the ethanol yields of each process were compared. The highest ethanol yield was obtained with simultaneous microbial saccharification and fermentation of exploded product at a steam pressure of 2 MPa and a steaming time of 3 min.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.41-44
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• 2021

In this paper, we propose a monitoring system that can monitor gas leakage concentrations in real time and forecast the amount of gas leaked after one minute. When gas leaks happen, they typically lead to accidents such as poisoning, explosion, and fire, so a monitoring system is needed to reduce such occurrences. Previous research has mainly been focused on analyzing explosion characteristics based on gas types, or on warning systems that sound an alarm when a gas leak occurs in industrial areas. However, there are no studies on creating systems that utilize specific gas explosion characteristic analysis or empirical urban gas data. This research establishes a deep learning model that predicts the gas explosion risk level over time, based on the gas data collected in real time. In order to determine the relative risk level of a gas leak, the gas risk level was divided into five levels based on the lower explosion limit. The monitoring platform displays the current risk level, the predicted risk level, and the amount of gas leaked. It is expected that the development of this system will become a starting point for a monitoring system that can be deployed in urban areas.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.2
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• pp.81-95
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• 2013

When collapse occurs due to explosion near a tunnel, fragmentation zone should be comprehended quickly to recover the function of the tunnel itself. In this study, a method to interpret explosion behavior and predict the fragmentation zone fast. For this purpose, the various 3D-meshes were generated using SolidWorks and explosion analyses were carried out using AUTODYN. The influence of explosion variables such as source location on fragmentation volume were examined by performing sensitivity analyses. Also, a training database for an artificial neural network analysis had been established and the optimal training model was selected, and the predicted results for fragmentation volume and radius were verified. The suggested method had demonstrated that it could be effective for the fast prediction of fragmentation zone.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.

• The Journal of Engineering Geology
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• v.10 no.2
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• pp.98-112
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• 2000

Discrimination between natural earthquakes and man-made explosions is very essential but critical matter in Seismology. In the CTBT Monitoring business, this is very crucial issue and sometimes could occur the international conflict. In this study, we analyzed seismic and infrasound data from Chulwon Seismo-Acoustic Array and would like to introduce routine data processing procedures at the Korea Institute of Geology, Mining and Materials(KIGAM) to discriminate the earthquakes and artificial explosions. We found analyzing acoustic data together with seismic data is very effective way to identify and discriminate man made explosion from natural earthquake. Recent earthquakes in and around the Korean Peninsula are concentrated in a narrow zone with N60-70$^{\circ}$W in southern Korea, and Pyungan and Hwanghae Province in northern Korea. The mechanism of 14 larger earthquakes in and around the Korean Peninsula since 1936 show predominant strike-slip faulting together with minor thrust component. This indicates horizontal compression is dominant in and around the Korean Peninsula.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.79-86
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• 2009

This paper proposed an algorithm for extracting spectrum gradient parameter to analyze the characteristics of natural seismic sounds and artificial seismic sounds. The experiment was performed in various area to raise the reliability. The characteristics of natural seismic sounds and artificial seismic sounds were analyzed by extracting gradient indexes of artificial seismic sounds and natural seismic sounds from the data of experiment by using the proposed algorithm. As a result of the experiment and the analysis, gradient indexes of natural seismic sounds were higher than that of artificial seismic sounds because natural seismic sounds had higher attenuation at high-frequency than artificial seismic sounds did and natural seismic sounds were concentrated in low-frequency band.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1999.10a
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• pp.72-82
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• 1999

The microearthquake and explosion events recorded in the seismic KNUE(Korea National University of Education) network were analyzed. The seismic data were recorded from Dec. 1997 to Dec. 1998. Total of 118 records consisted of 24 earthquake and 4 explosion events were instrumented at 6 stations. Spectral values increases as magnitude increases and the predominant frequency band expands to low frequency. zone as magnitude increases. Three-dimensional spectrograms(time frequency. amplitude) were also synthesized in order to discriminate microearthquakes and artificial underground explosions. The waves from microearthquakes show that frequency content of dominant amplitude appeared above 10 Hz and the discrimination can be performed in almost all the frequency domain of 3-d spectrogram.

• Korean Journal of Environmental Biology
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• v.20 no.4
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• pp.312-315
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• 2002

Impact of artificial illumination on zooplankton dynamics has been studied in Tongyong marine ranch during the period from August 1998 to August 1999. Monthly sampling has been carried out to collect zooplankters from both natural waters and artificially illuminated waters at night. A total of 48 taxa of zooplankton occurred during the study. Copepods showed the prosperity in species number with 21 species. Every sample from illuminated waters consisted of move than 15 species except February while less than 15 species in samples from natural waters during the winter. Benthic amphipods occurred abundantly in illuminated waters. Zooplankton abundance was revealed to be increased in illuminated waters mainly due to the gathering of amphipods (4,500 indiv. $m^{-3})$ in September and October. Twenty times of zooplankton abundance was recorded in illuminated waters when compared with that in natural ones in September due to the gathering: of amphipods and ten times by the explosion of N. scintillans in August 1999. However, no distinct difference in the abundance was observed between two waters in the winter. Zooplankton gathering with artificial illumination seemed to be effective in amphipods, while copepods were hardly affected by the artificial illumination at night.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.795-799
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• 2022

In recent years, with the rapid development of artificial intelligence technology and an explosion of interest in it, education on artificial intelligence is spreading to various fields. As a result, many universities are establishing artificial intelligence-related departments or expanding their quota. In line with this trend, University C has newly established the AI operation department in line with the industrial base in the region. In this paper, a curriculum was developed for the newly established AI operation department, and this curriculum was designed and developed focusing on subjects reflecting the demands of industries based on AIOps (Artificial intelligence for IT Operations). To this end, a consultative body was formed with industry experts, and opinions were collected through a survey.