• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.197-208
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• 2022

The existence of shallow bedrock and the desire to use underground space necessitate the use of blasting methods. The standard blasting method under water after drilling is associated with certain technical difficulties, including reduced detonation power, the use of a fixed charge per delay, and decoupling. However, there is no blasting method to replace the existing blasting method. In this paper, a dry hole charged with ANFO blasting is assessed while employing a dry hole pumping system to remove water from the drill borehole. Additional standard blasting is also utilized to compare the blasting performances of the two methods. The least-squares linear regression method is adopted to analyze the blasting vibration velocity quantitatively using the measured vibration velocity for each blasting method and the vibration velocity model as a function of the scaled distance. The results show that the dry hole charged with ANFO blasting will lead to greater damping of the blasting vibration, more energy dissipation to crush the surrounding rock, and closer distances for the allowable velocity of the blasting vibration. Also, standard blasting shows much longer influencing distances and a wider range of the blasting pattern. The pilot test confirms the blasting efficiency of dry hole charged with ANFO blasting.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.493-504
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• 2023

The Republic of Korea is located far from the boundary of the earthquake plate, and the intra-plate earthquake occurring in these areas is generally small in size and less frequent than the interplate earthquake. Nevertheless, as a result of investigating and analyzing earthquakes that occurred on the Korean Peninsula between the past two years and 1904 and earthquakes that occurred after observing recent earthquakes on the Korean Peninsula, it was found that of a magnitude of 9. In this paper, the Korean Peninsula Historical Earthquake Record (2 years to 1904) published by the National Meteorological Research Institute is used to analyze the relationship between earthquakes on the Korean Peninsula and statistical self-similarity. In addition, the problem solved through this paper was the first to investigate the relationship between earthquake data occurring on the Korean Peninsula and statistical self-similarity. As a result of measuring the degree of self-similarity of earthquakes on the Korean Peninsula using three quantitative estimation methods, the self-similarity parameter H value (0.5 < H < 1) was found to be above 0.8 on average, indicating a high degree of self-similarity. And through graph visualization, it can be easily figured out in which region earthquakes occur most often, and it is expected that it can be used in the development of a prediction system that can predict damage in the event of an earthquake in the future and minimize damage to property and people, as well as in earthquake data analysis and modeling research. Based on the findings of this study, the self-similar process is expected to help understand the patterns and statistical characteristics of seismic activities, group and classify similar seismic events, and be used for prediction of seismic activities, seismic risk assessments, and seismic engineering.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.253-261
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• 2024

Olivine, a major mineral in the upper mantle with strong intrinsic elastic anisotropy, plays a crucial role in seismic anisotropy in the mantle, primarily through its lattice preferred orientation (LPO). Despite this, the influence of the microstructure of mylonitic rocks on seismic anisotropy remains inadequately understood. Notably, there is a current research gap concerning seismic anisotropy directly inferred from mylonitic peridotite massifs in Korea. In this study, we introduce the deformation microstructure and LPO of olivine in the mantle shear zone. We calculate the characteristics of seismic anisotropy based on the degree of deformation (proto-mylonite, mylonite, ultra-mylonite) and establish correlations between these characteristics. Our findings reveal that the seismic anisotropy resulting from the olivine LPO in the ultra-mylonitic rock appears to be the weakest, whereas the seismic anisotropy resulting from the olivine LPO in the proto-mylonitic rock appears to be the strongest. The results demonstrate a gradual decrease in seismic anisotropy as the fabric strength (J-index) of olivine LPO diminishes, irrespective of the specific pattern of olivine's LPO. Moreover, all samples exhibit a polarization direction of the fast S-wave aligned subparallel to the lineation. This suggests that seismic anisotropy originating from olivine in mylonitic peridotites is primarily influenced by fabric strength rather than LPO type. Considering these distinctive characteristics of seismic anisotropy is expected to facilitate comparisons and interpretations of the internal mantle structure and seismic data in the Yugu area, Gyeonggi Massif.

• Information Systems Review
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• v.21 no.4
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• pp.175-195
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• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.3
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• pp.253-261
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• 2005

Catalytic wet oxidation of ppm levels of trichloroethylene (TCE) in water has been conducted using $TiO_2$-supported cobalt oxides at a given temperature and weight hourly space velocity. 5% $CoO_x/TiO_2$ might be the most promising catalyst for the wet oxidation at $36^{\circ}C$ although it exhibited a transient behavior in time on-stream activity. Not only could the bare support be inactive for the wet decomposition reaction, but no TCE removal also occurred by the process of adsorption on $TiO_2$ surface. The catalytic activity was independent of all particle sizes used, thereby representing no mass transfer limitation in intraparticle diffusion. Characterization of the $CoO_x$ catalyst by acquiring XPS spectra of both fresh and used Co surfaces gave different surface spectral features of each $CoO_x$. Co $2p_{3/2}$ binding energy of Co species exposed predominantly onto the outermost surface of the fresh catalyst appeared at 781.3 eV, which is very similar to the chemical states of $CoTiO_x$ such as $Co_2TiO_4$ and $CoTiO_3$. The spent catalyst possessed a 780.3 eV main peak with a satellite structure at 795.8 eV. Based on XPS spectra of reference Co compound, the TCE-exposed Co surfaces could be assigned to be in the form of mainly $Co_3O_4$. XRD measurements indicated that the phase structure of Co species in 5% $CoO_x/TiO_2$ catalyst even before reaction is quite comparable to the diffraction lines of external $Co_3O_4$ standard. A model structure of $CoO_x$ present on titania surfaces would be $Co_3O_4$, encapsulated in thin-film $CoTiO_x$ species consisting of $Co_2TiO_4$ and $CoTiO_3$, which may be active for the decomposition of TCE in a flow of water.

• Journal of the Mineralogical Society of Korea
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• v.20 no.3
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• pp.193-201
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• 2007

We used $^6Li$ and $^7Li$ MAS NMR to investigate the fate and local environments of Li in the interlayer of clay minerals such as hectorite, Woming-montmorillonite, beidellite, and lepidollite at room and high ($250^{\circ}C$) temperature. Although $^6Li$ NMR spectra show narrower peaks than those of $^7Li$ NMR, S/N ratio is low and there are no obvious differences in chemical shifts suggesting that it is difficult to apply $^6Li$ NMR to have information on the local environments of Li in the clay interlayers. $^7Li$ NMR spectra, however, show changes in the peak width and quadrupole patterns providing information on the local environments of Li in the interlayer even though changes in the chemical shift are not observed. In montmorillonite, two different local environments of Li are observed; one has a narrow peak with typical quadrupole patterns whereas another has a broad peak without those of the patterns. Changes in the peak width is also observed from broad to narrow in the $^7Li$ NMR spectra for beidellite but not for hectorite at high temperature. Our results suggest that the peak width change in the broad peak is attributed to the coordination changes in the water molecules around Li which is tightly bonded on the basal oxygen of Si tetrahedra as inner-sphere complexes. The narrow peak in montmorillnoite can be assigned to the Li bended as outer-sphere complexes.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.4
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• pp.186-194
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• 2013

We investigated the concentrations and distribution patterns of 17 polychlorinated dibenzo-p-dioxins/dibenzofurans(PCDD/Fs), 12 dioxin-like polychlorinated biphenyls(DL-PCBs) and 24 polybrominated diphenyl ethers(PBDEs) in sediments from Ulsan Bay in Korea. The concentrations of PCDD/Fs, DL-PCBs, and PBDEs in 33 sediment samples ranged from 0.11 to 4.86($1.81{\pm}1.04$) pg $WHO_{2005}$-TEQ $g^{-1}$ dry weight(dw), 0.06 to 44.2($4.02{\pm}7.99$) pg $WHO_{2005}$-TEQ $g^{-1}$ dw, and 2.81 to 63.8($19.4{\pm}13.9$) ng $g^{-1}$ dw, respectively. DL-PCBs had dominant contributions(mean, 88%) of total TEQ concentrations in sediment. The concentrations of target compounds in inner locations were higher than those in outer locations in Ulsan Bay (p<0.05). The dominant contribution of highly chlorinated DD/Fs in sediment was associated with combustion process from industrial complexes. Distribution pattern of DL-PCBs was similar with those of commercial PCB products. BDE209 was a dominant congener in sediment, suggesting high use amount of commercial deca-BDE product in surrounding areas.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Clean Technology
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• v.29 no.4
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• pp.255-261
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• 2023

Power semiconductors are semiconductors used for power conversion, transformation, distribution, and control. Recently, the global demand for high-voltage power semiconductors is increasing across various industrial fields, and optimization research on high-voltage IGBT components is urgently needed in these industries. For high-voltage IGBT development, setting the resistance value of the wafer and optimizing key unit processes are major variables in the electrical characteristics of the finished chip. Furthermore, the securing process and optimization of the technology to support high breakdown voltage is also important. Etching is a process of transferring the pattern of the mask circuit in the photolithography process to the wafer and removing unnecessary parts at the bottom of the photoresist film. Ion implantation is a process of injecting impurities along with thermal diffusion technology into the wafer substrate during the semiconductor manufacturing process. This process helps achieve a certain conductivity. In this study, dry etching and wet etching were controlled during field ring etching, which is an important process for forming a ring structure that supports the 3.3 kV breakdown voltage of IGBT, in order to analyze four conditions and form a stable body junction depth to secure the breakdown voltage. The field ring ion implantation process was optimized based on the TEG design by dividing it into four conditions. The wet etching 1-step method was advantageous in terms of process and work efficiency, and the ring pattern ion implantation conditions showed a doping concentration of 9.0E13 and an energy of 120 keV. The p-ion implantation conditions were optimized at a doping concentration of 6.5E13 and an energy of 80 keV, and the p+ ion implantation conditions were optimized at a doping concentration of 3.0E15 and an energy of 160 keV.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.