• Korea Trade Review
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• v.46 no.2
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• pp.19-38
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• 2021

This study aims to analyze the foreign asset leverage effect on Korean oil & gas companies' foreign profits and to maintain the appropriate foreign asset volume for reducing exchange risk. For a long time, large Korean companies, including oil companies, overheld foreign currency liabilities. For this reason, most large companies have been burdened to hedge exchange risk and this excess limit holding deteriorated total profit and reduced foreign currency asset management efficiency. Our paper proceeds in presenting a three-stage analysis considering diversified exchange risk factors through estimation on transformation of foreign transactions a/c including annual trends of foreign asset and industry specifics. We also supplement incomplete the estimation method through a practical hedging case investigation. Our research parts are differentiated on the analyzing four periods considering period-specifics The FER value of the oil firms ranged from -0.3 to +2.3 over the entire period. The results of the FER Value are volatile and irregular; those results do not represent the industry standard comparative index. The Korean oil firms are over the credit limit without accurate prediction and finance high interest rate funds from foreign-owned banks on the basis on a biased relationship. Since the IMF crisis, liabilities of global firms have decreased. Above all, oil firms need to finance a minimum limit without opportunity losses on the demand forecast and prepare for uncertainty in the market. To reduce exchange risk from the over-the-limit position, we must consider factors that affect the corporate exchange risk on the entire business process, including the contract phase.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.230-245
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• 2023

The importance of evaluating the safety of Automated Vehicles (AV) is increasing with the advances in autonomous driving technology. Accordingly, an evaluation scenario that defines in advance the situations AV may face while driving is being used to conduct efficient stability evaluation. On the other hand, the single scenarios currently used in conventional evaluations address limited situations within short segments. As a result, there are limitations in evaluating continuous situations that occur on real roads. Therefore, this study developed a set of multiple scenarios that allow for continuous evaluation across entire sections of roads with diverse geometric structures to assess the safety of AV. In particular, the conditions for connecting individual scenarios were defined, and a methodology was proposed for developing concrete multiple scenarios based on the scenario evaluation procedure of the PEGASUS project. Furthermore, a simulation was performed to validate the practicality of these multiple scenarios.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.71-79
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• 2024

In compliance with environmental regulations at sea and the introduction of unmanned autonomous ships, electric propulsion ships are garnering significant attention. Induction machines used as propulsion electric motor (PEM) have maintenance advantages, but speed control is very complicated and difficult. One of the most commonly used techniques for speed control is DTC (direct torque control). DTC is simple in the reference frame transformation and the stator flux calculation. Meanwhile, two-level and three-level voltage source inverters (VSI) are predominantly used. The three-level VSI has more flexibility in voltage space vector selection compared to the two-level VSI. In this paper, speed is controlled using the DTC method based on the specifications of the PEM. The speed controller employs a PI controller with anti-windup functionality. In addition, the characteristics of the two-level VSI and three-level VSI are compared under identical conditions. It was confirmed through simulation that proper control of speed and torque has been achieved. In particular, the torque ripple was small and control was possible with a low DC voltage at low speed in the three-level VSI. The study confirmed that the application of DTC, using a three-level VSI, contributes to enhancing the system's response performance.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.65-77
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• 2024

This study aimed to propose effective ways to integrate CLO into educational settings by conducting a comparative analysis of pattern functions in YUKA and CLO, specifically focusing on skirt prototypes and variations. CLO, being a 3D virtual sample CAD tool, is mainly used in education to facilitate the creation of 3D virtual clothing. In order to explore the applicability of CLO's pattern functions in pattern education, CAD education experts were asked to produce two types of skirt prototypes and two skirt variations. Subsequently, in-depth interviews were conducted. In addition, the skirt pattern creation process was recorded on video and used for comparative analysis of YUKA and CLO pattern functions. The comparison revealed that CLO provides the pattern tools necessary for drafting skirt prototypes. The learning curve for acquiring the skills necessary for drafting and transforming skirt prototypes was found to be relatively shorter for CLO compared to YUKA. In addition, due to CLO's surface-based pattern drawing method, it is difficult to move or copy only specific parts of the outline, and there are some limitations in drawing right angle lines. In the pattern transformation process, CLO's preview function proved to be advantageous, and it was highly rated on user convenience due to the intuitive UI. Thus, CLO shows promise for pattern drafting education and is deemed to have high scalability as it is directly linked to 3D virtual clothing.

• Korean Chemical Engineering Research
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• v.43 no.4
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• pp.503-510
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• 2005

In this research, composite alumina was prepared to add the various promoters by sol-gel method and examined its thermal stability. After sintering at $1,200^{\circ}C$, the thermal stability resulted in following order, $Si{\fallingdotseq}La$ > Ti > $Ba{\fallingdotseq}Ce$ > Y > $Zr{\fallingdotseq}Mg$, in accordance with adding the promoters. Especially in case of silica-added alumina, a phase transformation temperature to ${\alpha}$-alumina increased about $150^{\circ}C$ and after sintering at $1,200^{\circ}C$, it showed to maintain in ${\gamma}$-form and ${\delta}$-form alumina phase. Also it showed an increase of surface area from $3m^2/g$ to $71m^2/g$ compared with pure ${\alpha}$-alumina. In the case of silicaadded alumina, the characterization change of this alumina particle resulted in a delay of phase transformation because Si-O-Al bond was increased when sintered at high temperature. In case of lanthanum-added alumina, there was a sintering delay phenomenon in inter-particles as $LaAlO_3$ structure existed. The existence of lanthanum structure was confirmed by XRD and XPS analysis. It appeared on the alumina surface as $La_2O_3$ structure when it was sintered under $1,000^{\circ}C$, as the perovskite structure of $LaAlO_3$ at above $1,000^{\circ}C$ and as the magneto-plumbite structure of $LaAl_{11}O_{18}$ at above $1,300^{\circ}C$.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.2
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• pp.165-171
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• 2013

Due to the difficulty in considering dynamic load in the view point of a computer resource and computing time, it is common that external load is assumed as ideal static loads. However, structural analysis under static load cannot guarantee the safety of design of the structures under dynamic loadings. Recently, the systematic method to construct equivalent static load from the given dynamic load has been proposed. Previous study has calculated equivalent static load through the optimization procedure under displacement constraints. However, previously reported works to distribute equivalent static load were based on ad-hoc methods. Improper selection of equivalent static loading positions may results in unreliable prediction of structural design. The present study proposes the selection method of the proper locations of equivalent static loads to dynamically applied loads when we consider transient dynamic structural problems. Moreover, it is appropriate to take into account the stress constraint as well as displacement constraint condition for the safety design. But the previously reported studies of equivalent static load design methods considered only displacement constraint conditions but not stress constraint conditions. In the present study we consider not only displacement constraint but also stress constraint conditions. Through a few numerical examples, the efficiency and reliability of proposed scheme is verified by comparison of the equivalent stress between equivalent static loading and dynamic loading.

• IMMUNE NETWORK
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• v.1 no.2
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• pp.151-161
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• 2001

Background: Inactivation in p53 tumor suppressor gene through a point mutation and deletion is one of the most frequent genetic changes found in human cancer, with 50% of an incidence. This high rate of mutation mostly suggests that the gene plays a central role in the development of cancer and the mutations detected so far were found in exons 5 to 8. Mutation of p53 locus produced accumulation of abnormal p53 protein, and negative regulation of cell proliferation and transcriptional activation as a suppressor of transformation were lost. In addition, inhibition of its normal cellular function of wild-type by mutant is an important step in tumorigenesis. Method: 4 colon cancer cell lines (SNU C1, C2A, C4, C5) were examined for mutation in exons 5 to 8 of the p53 tumor suppressor gene by PCR-SSCP analysis and expression pattern by western blotting and immunoprecipitation. p53-mediated transactivation ability were examined by CAT assay and base substitution of p53 in SNU C2A cell were detected by DNA sequencing. Results: 1) SNU C2A cell and SNU C5 cell were detected mobility shifts each in exon 5 and exon 7 of p53 gene by the PCR-SSCP method, implicating being of p53 mutation. 2) 3 colon cancer cell lines (SNU C1, SNU C2A, SNU C5) expressed wild type and mutant type p53 protein. 3) In northern blot experiment, SNU C2A and SNU C5 cell expressed high level of p53 mRNA. 4) Results of p53-mediated transactivation in colon cancer cell lines by CAT assay represented only SNU C2A cell has transcriptional activity. 5) DNA sequencing in SNU C2A cell showed missense mutation in codon 179 of one allele, histidine to arginine and wild type p53 in the other allele. Conclusion: Colon cancer cell lines showed correlation with mutation in p53 gene and accumulation of abnormal p53 protein. Colon cancer cell SNU C2A retained p53-mediated transactivation as heterozygous p53 with one mutant allele in 179 codon and the other wild-type allele.