• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.4
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• pp.233-252
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• 2021

This paper is a study to improve the efficiency of mixing technology in the shield TBM chamber. Currently, the number of construction cases using the TBM method is increasing in Korea. According to the increasing use of TBM method, research on TBM method such as Disc Cutter, Cutter bit, and Segment also shows an increasing trend. However, there is little research on the mixing efficiency in chamber and chamber. In order to improve the smooth soil treatment and the behavior of the excavated soil, a study was conducted on the change of the mixing efficiency according to the effective mixing bar arrangement in the chamber. In the scale model experiment, the ground was composed using plastic materials of different colors for ease of identification. In addition, the mixing bar arrangement was different and classified into 4 cases, and the particle size distribution was classified into single particle size and multiple particle size, and the experiment was conducted with a total of 8 cases. The rotation speed of the cutter head of all cases was the same as 5 RPM, and the experiment time was also carried out in the same condition, 1 minute and 30 seconds. In order to check the mixing efficiency, samples at the upper, middle (left or right), and lower positions of each case were collected and analyzed. As a result of the scaled-down model experiment, the mixing efficiency of Case 4 and Case 4-1 increased compared to Case 1 and Case 1-1, which are actually used. Accordingly, it is expected that the mixing efficiency can be increased by changing the arrangement of the mixing bar in the chamber, and it is considered to be effective in saving air as the mixing efficiency increases. Therefore, this study is considered to be an important indicator for the use of shield TBM in Korea.

• 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.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.2
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• pp.48-54
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• 2021

Purpose In ¹⁸F-FDG PET/CT, the absorption of ¹⁸F-FDG due to the activation of Brown Adipose Tissue (BAT) greatly interferes with the discrimination of lymph node malignant metastasis. Warming the patient's body temperature before and after injection of ¹⁸F-FDG to prevent FDG absorption by BAT is a safe and non-pharmacological approach. The purpose of this study was to identify and select patients with a high potential for BAT activation in advance, and to investigate whether BAT can inhibit FDG absorption when the body temperature is raised for a short time by directly applying heat to the target patient. Materials and Methods Among the patients who underwent ¹⁸F-FDG PET/CT at the National Cancer Center from January 2020 to December 2020, 825 female patients (415 in the thermal group, 410 in the non-thermal group) under 50 years old were included. The thermal group was administered heat for 10 minutes before injection of ¹⁸F-FDG. For statistical analysis, the Z test comparing the ratios between the two groups was used, and logistic regression analysis was performed to correct for important variables (BMI, outdoor temperature, blood sugar) according to the results of the previous retrospective study. Results Among 825 patients, 19 patients with BAT activated (Thermal group: 5(1.2%), Non-thermal group: 14(3.41%)) accounted for 2.3% of the total. As a result of performing the Z test to compare the ratios between the two groups, the activation of BAT in the thermal group was significantly decreased (P=0.034). In the univariate logistic regression analysis, the activation of BAT was also decreased in the thermal group (OR: 0.34, P<0.05). In the multivariate results, BAT activation increased in patients younger than 45 years old (OR: 4.46, P<0.05) and outdoor temperature less than 13.2 degrees (OR: 9.97, P<0.05). BAT activation tended to decrease in the thermal group, but there was no significant difference (OR: 0.37, P=0.066). Conclusion We confirmed that the activation of BAT tends to decrease by 62.5% in the group subjected to the thermal method, and it will be of great help in preventing FDG absorption of BAT more effectively in the future.

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.281-302
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• 2023

Carbon is not only an essential element for life but also a key player in climate change. The radiocarbon (¹⁴C) analysis using accelerator mass spectrometry (AMS) is a powerful tool not only to understand the carbon cycle but also to track pollutants derived from fossil carbon, which have a distinct radiocarbon isotope ratio (Δ¹⁴C). Many studies have reported Δ¹⁴C of carbon compounds in streams, rivers, rain, snow, throughfall, fine particulate matter (PM_2.5), and wastewater treatment plant effluents in South Korea, which are reviewed in this manuscript. In summary, (1) stream and river carbon in South Korea are largely derived from the chemical weathering of soils and rocks, and organic compounds in plants and soils, strongly influenced by precipitation, wastewater treatment effluents, agricultural land use, soil water, and groundwater. (2) Unprecedentedly high Δ¹⁴C of precipitation during winter has been reported, which can directly and indirectly influence stream and river carbon. Although we cannot exclude the possibility of local contamination sources of high Δ¹⁴C, the results suggest that stream dissolved organic carbon could be older than previously thought, warranting future studies. (3) The ¹⁴C analysis has also been applied to quantify the sources of forest throughfall and PM_2.5, providing new insights. The ¹⁴C data on a variety of ecosystems will be valuable not only to track the pollutants derived from fossil carbon but also to improve our understanding of climate change and provide solutions.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.121-137
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• 2022

The purpose of this study is to present the necessary success factors and strategies for high-tech social ventures and stakeholders in the related ecosystem by empirically identifying factors that affect their sustainable performance. Based on prior research, the dimensions of three performance factors were presented: core technology competency, core business competency, and social mission orientation. Then, such sub-dimensions such as technology innovation orientation, R&D capability, business model, customer orientation, social network, and social mission pursuit were derived. For empirical analysis, a survey was conducted on domestic high-tech social ventures, and the significance of the hypothesis was tested through PLS-structural equation analysis of the collected 243 valid data. As a result, it was found that the technology innovation orientation was embedded as an abstract organizational and cultural characteristic in the high-tech social venture, which is a research sample, and thus did not significantly affect the dependent variable. In other words, aiming for the latest cutting-edge technology alone cannot affect performance, and it is a result of proving the need for substantial influencing factors that can strengthen it. On the other hand, the business model had a significant effect only on social performance, which is presumed to be the limitation of measurement tools developed for social enterprises, and the results of additional multi-group analysis to determine the cause also supported the basis for this estimation. Excluding the previous two performance factors, R&D competency, customer orientation, social network, and social mission pursuit were all found to have a significant positive (+) effect on social and economic performance. This study laid a foundation for related research by identifying high-tech social ventures emerging in the ecosystem of a social economy and expanded empirical research models related to the performance of existing social enterprises and social ventures. However, in the research method or process, there were limitations such as factor derivation or verification for balance of dual performance, subjective measurement method, and sample representativeness. It is expected that more in-depth follow-up studies will continue by supplementing future limitations and designing improved research models.

• Journal of Korean Society of Forest Science
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• v.103 no.3
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• pp.339-352
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• 2014

The study was carried out to analyze vegetation structure of Pinus densiflora and Quercus mongolica forests located in Jochimryeong to Shinbaeryeong of the Baekdudaegan mountain range. The survey for 50 plots was conducted from April 2012 to August 2013 in the permanent plots ($100m{\times}100m$) using phytosociological analysis. As a result, the vegetations were classified into five vegetation units. In species composition, they were classified into Q. mongolica community group divided into 2 community such as Fraxinus rhynchophylla community and Carpinus cordata community, F. rhynchophylla community was subdivided Pinus densiflor group (into Euonymus sachalinensis subgroup, Vitis coignetiae subgroup) and Juglans mandshurica group. C. cordata community was subdivided Acer komarovii group and Betula ermanii group. In terms of importance value, P. densiflora and Q. mongolica were more than 20% respectively. P. densiflora was found to have the highest relative coverage. Analysis of interspecific association showed four types which were coincident with differential species and character species on the constancy table. Based on the diameter class distribution, P. densiflora forest presented a normal distribution pattern except for other species which showed a reverse Jshaped distribution pattern, therefore P. densiflora forest would likely be replaced by Q. mongolica forests. While in Q. mongolica forest, diameter class distribution of all species population presented a reverse J-shaped distribution pattern, therefore Q. mongolica forest could likely remain in the future.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.465-482
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• 2016

Previous researches have introduced various ways to analyze the impact of standardization on innovation while the works are not only small in number but based on interview or case study. This paper addresses the impact of standardization activities within South Korean industries on technological innovation applying an empirical analysis of standardization activities and technological innovation. Drawing on Korean Industrial Standards Classification from panel data of 2003 to 2012, we employed corresponding data of each industrial classification: Number of standards, Accumulated number of standards, Number of patents applied in Korea, Sales, Operational profit, Intangible asset, and R&D invest. In the first model, we run panel data models employing the number of patents applied in Korea as an independent variable, and the number of standards, accumulated number of standards, sales, and operational profit as dependent variables to observe industrial impacts upon the relationship between standards and patents, along with time lagged consideration. The result shows that number of standards are revealed to have a negative influence on patent applications in the year of research, and no significant effect appears for the next two years while positive effect shows up on the third year. Meanwhie, accumulated number of standards turned out to have positive effects on patent applications in Korea. This implies it takes time for innovation subjects to embrace newly established standards while having a significant amount of positive effect on technological innovation in the long term. In the second model, we use canonical correlation analysis to find industrial-wide characteristics. The result of this model is equivalent to the result of panel data analysis except in a few industries, where some industry specific characteristics appear. The implications of our results present that Korean policy makers have to take account of industrial effects on standardization to promote technological innovation.

• The korean journal of orthodontics
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• v.35 no.6 s.113
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• pp.475-484
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• 2005

This study was performed to investigate the reproducibility of the horizontal and midsagittal planes, and to suggest a stable coordinate system for three-dimensional (3D) cephalometric analysis. Eighteen CT scans were taken and the coordinate system was established using 7 reference points marked by a volume model, with no more than 4 points on the same plane. The 3D landmarks were selected on V works (Cybermed Inc., Seoul, Korea), then exported to V surgery (Cybermed Inc., Seoul, Korea) to calculate the coordinate values. All the landmarks were taken twice with a lapse of 2 weeks. The horizontal and midsagittal planes were constructed and its reproducibility was evaluated. There was no significant difference in the reproducibility of the horizontal reference planes, But, FH planes were more reproducible than other horizontal planes. FH planes showed no difference between the planes constructed with 3 out of 4 points. The angle of intersection made by 2 FH planes, composed of both Po and one Or showed less than $1^{\circ}$ difference. This was identical when 2 FH planes were composed of both Or and one Po. But, the latter cases showed a significantly smaller error. The reproducibility of the midsagittal plane was reliable with an error range of 0.61 to $1.93^{\circ}$ except for 5 establishments (FMS-Nc, Na-Rh, Na-ANS, Rh-ANS, and FR-PNS). The 3D coordinate system may be constructed with 3 planes; the horizontal plane constructed by both Po and right Or; the midsagittal plane perpendicular to the horizontal plane, including the midpoint of the Foramen Spinosum and Nc; and the coronal plane perpendicular to the horizontal and midsagittal planes, including point clinoidale, or sella, or PNS.