• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.41-52
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• 2022

Although domestic public libraries achieved quantitative growth based on the 1st and 2nd comprehensive library development plans, there were some qualitative shortcomings, and various studies have been conducted to improve them. Most of the preceding studies have limitations in that they are limited to social and economic factors and statistical analysis. Therefore, in this study, by applying the spatiotemporal concept to quantitatively calculate the decrease in public library loan demand due to rainfall and heatwave, by clustering areas with high demand for book loan due to weather changes and areas where it is not, factors inside and outside public libraries and After the combination, changes in public library loan demand according to weather changes were analyzed. As a result of the analysis, there was a difference in the decrease due to the weather for each public library, and it was found that there were some differences depending on the characteristics and spatial location of the public library. Also, when the temperature was over 35℃, the decrease in book loan demand increased significantly. As internal factors, the number of seats, the number of books, and area were derived. As external factors, the public library access ramp, cafe, reading room, floating population in their teens, and floating population of women in their 30s/40s were analyzed as important variables. The results of this analysis are judged to contribute to the establishment of policies to promote the use of public libraries in consideration of the weather in a specific season, and also suggested limitations of the study.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.1
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• pp.88-93
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• 2024

To evaluate the possibility as a multi-level memory medium for the Ge₂Sb₂Te₅/TiN/W-doped Ge₂Sb₂Te₅ cell structure, the crystallization rate and stabilization characteristics according to voltage (V)- and current (I)- pulse sweeping were investigated. In the cell structures prepared by a magnetron sputtering system on a p-type Si (100) substrate, the Ge₂Sb₂Te₅ and W-doped Ge₂Sb₂Te₅ thin films were separated by a barrier metal, TiN, and the individual thicknesses were varied, but the total thickness was fixed at 200 nm. All cell structures exhibited relatively stable multi-level states of high-middle-low resistance (HR-MR-LR), which guarantee the reliability of the multilevel phase-change random access memory (PRAM). The amorphousto-multilevel crystallization rate was evaluated from a graph of resistance (R) vs. pulse duration (T) obtained by the nanoscaled pulse sweeping at a fixed applied voltage (12 V). For all structures, the phase-change rates of HR→MR and MR→LR were estimated to be approximately t<20 ns and t<40 ns, respectively, and the states were relatively stable. We believe that the doublestack structure of an appropriate Ge-Sb-Te film separated by barrier metal (TiN) can be optimized for high-speed and stable multilevel PRAM.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.50 no.1
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• pp.27-34
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• 2024

Objectives: Surgical intervention for removal of an impacted third molar can lead to significant pain and swelling. Corticosteroids show promise for mitigating postoperative sequelae across various surgical contexts. The use of corticosteroids following minor oral surgery, though controversial, has already been proven effective. However, little research has explored peroral prescription of corticosteroids despite its convenience for outpatients and for non-surgeons like implantologists and periodontists and others who don't have access to needle injections. The aim of this study was to address a void in the literature by comparing the effects of two styles of preoral administration of prednisolone after surgical removal of the mandibular third molar and to determine which style minimizes postoperative sequelae. Materials and Methods: A randomized, split-mouth clinical study was conducted to investigate the efficacy of two different styles of preoral prednisolone in mitigating postoperative sequelae following surgical extraction of impacted mandibular third molars. Fifteen participants were enrolled in the study. Random selection was used to determine the prescription style for the right and left mandibular arch. Group A included those who received a single dose of prednisolone 25 mg, while group B received prednisolone 5 mg postoperatively for a period of three days (5 mg three times/day on the first postoperative day, 5 mg twice/day on the second postoperative day; 5 mg once/day on the third postoperative day). Results: There was a significant difference in the distance between the corner of the mouth and tragus, which decreased with the time interval with respect to group B when compared to group A. Conclusion: The present study showed that a three-day tapered dose of prednisolone postoperatively was more effective in reducing post-extraction sequelae than a single-dose regimen.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.

• Survey Research
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• v.13 no.1
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• pp.135-158
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• 2012

Korean telephone surveys have been based on landline telephone directory or RDD(Random Digit Dialing) method. These days, however, there has been an increase of the households with no landline, or households with the line but not willing to register in the directory. Moreover, it is hard to contact young people or office workers who are usually staying out of home in the daytime. Due to these issues above, the predictability of election polls gets weaker. Especially, low accessibility to those who stay out of home when the poll's done, results in predictions with positive inclination toward conservatism. A solution to resolve this problem is to contact respondents by using both mobile and landline phones-via landline phone to those who are at home and via mobile phone to those who are out of home in the daytime(Mixed Mode Survey, hereafter MMS). To conduct MMS, 1) we need to obtain the sampling frames for the landline and mobile surveys, and 2) we need to decide the proportion of sample size of both. In this paper, we propose a heuristic method for conducting MMS. The method uses RDD for the landline phone survey, and the access panel list for the mobile phone survey. The proportion of sample sizes between landline and mobile phones are determined based on the 'Lifestyle and Time Use Study' conducted by Statistics Korea. As a case study, 4 election polls were conducted in the periods of the special election for the mayor of Seoul on Oct 26th, 2011. From the initial 3 polls, reactions and responses regarding the issues raised during the survey period were appropriately covered, and the final poll showed a very close prediction to the real election result.

• Applied Microscopy
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• v.38 no.4
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• pp.279-284
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• 2008

The phase change materials have been extensively used as an optical rewritable data storage media utilizing their phase change properties. Recently, the phase change materials have been spotlighted for the application of non-volatile memory device, such as the phase change random access memory. In this work, we have investigated the crystallization behavior and microstructure analysis of In-Sb-Te (IST) thin films deposited by RF magnetron sputtering. Transmission electron microscopy measurement was carried out after the annealing at $300^{\circ}C$, $350^{\circ}C$, $400^{\circ}C$ and $450^{\circ}C$ for 5 min. It was observed that InSb phases change into $In_3SbTe_2$ phases and InTe phases as the temperature increases. It was found that the thickness of thin films was decreased and the grain size was increased by the bright field transmission electron microscopy (BF TEM) images and the selected area electron diffraction (SAED) patterns. In a high resolution transmission electron microscopy (HRTEM) study, it shows that $350^{\circ}C$-annealed InSb phases have {111} facet because the surface energy of a {111} close-packed plane is the lowest in FCC crystals. When the film was heated up to $400^{\circ}C$, $In_3SbTe_2$ grains have coherent micro-twins with {111} mirror plane, and they are healed annealing at $450^{\circ}C$. From the HRTEM, InTe phase separation was occurred in this stage. It can be found that $In_3SbTe_2$ forms in the crystallization process as composition of the film near stoichiometric composition, while InTe phase separation may take place as the composition deviates from $In_3SbTe_2$.

• Korean Journal of Materials Research
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• v.11 no.5
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• pp.367-371
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• 2001

In this paper, theS $r_2$(T $a_{1-x}$ , N $b_{x}$)$_2$ $O_{7}$(STNO) films among ferroelectric materials having a low dielectric constant for metal-ferroelectric-semiconductor field effect transistor(MFS-FET) were discussed. The STNO thin films were deposited on p-type Si(100) at room temperature by co-sputtering with S $r_2$N $b_2$ $O_{7(SNO)}$ ceramic target and T $a_2$ $O_{5}$ ceramic target. The composition of STNO thin films was varied by adjusting the power ratios of SNO target and T $a_2$ $O_{5}$ target. The STNO films were annealed at 8$50^{\circ}C$, 90$0^{\circ}C$ and 9$50^{\circ}C$ temperature in oxygen ambient for 1 hour. The value of x has significantly influenced the structure and electrical properties of the STNO films. In the case of x= 0.4, the crystallinity of the STNO films annealed at 9$50^{\circ}C$ was observed well and the memory windows of the Pt/STNO/Si structure were 0.5-8.3 V at applied voltage of 3-9 V and leakage current density was 7.9$\times$10$_{08}$A/$\textrm{cm}^2$ at applied voltage of -5V.of -5V.V.V.

• Journal of the Korean Magnetics Society
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• v.23 no.4
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• pp.122-125
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• 2013

Perpendicular magnetic anisotropy (PMA) is the phenomenon of magnetic thin film which is preferentially magnetized in a direction perpendicular to the film's plane. Amorphous multilayer with PMA has been studied as the good candidate to realization of high density STT-MRAM (Spin Transfer Torque-Magnetic Random Access Memory). The current issue of high density STT-MRAM is a decrease in the switching current of the device and an application of amorphous materials which are most suitable devices. The amorphous ferromagnetic material has low saturated magnetization, low coercivity and high thermal stability. In this study, we presented amorphous ferromagnetic multilayer that consists of an amorphous alloy CoSiB and a nonmagnetic material Pd. We investigated the change of PMA of the $[CoSiB\;t_{CoSiB}/Pd\;1.3nm]_5$ multilayer ($t_{CoSiB}$ = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 nm, and $t_{Pd}$ = 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 nm) and $[CoSiB\;0.3nm/Pd\;1.3nm]_n$ multilayer (n = 3, 5, 7, 9, 11, 13). This multilayer is measured by VSM (Vibrating Sample Magnetometer) and analyzed magnetic properties like a coercivity ($H_c$) and a magnetization ($M_s$). The coercivity in the $[CoSiB\;t_{CoSiB}\;nm/Pd\;1.3nm]_5$ multi-layers increased with increasing $t_{CoSiB}$ to reach a maximum at $t_{CoSiB}$ = 0.3 nm and then decreased for $t_{CoSiB}$ > 0.3 nm. The lowest saturated magnetization of $0.26emu/cm^3$ was obtained in the $[CoSiB\;0.3nm/Pd\;1.3nm]_3$ multilayer whereas the highest coercivity of 0.26 kOe was obtained in the $[CoSiB\;0.3nm/Pd\;1.3nm]_5$ mutilayer. Additional Pd layers did not contribute to the perpendicular magnetic anisotropy. The single domain structure evolved in to a striped multi-domain structure as the bilayer repetition number n was increased above 7 after which (n > 7) the hysteresis loops had a bow-tie shapes.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.