• Journal of the Korean Electrochemical Society
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• v.9 no.3
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• pp.124-131
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• 2006

For commercialization of molten carbonate fuel cell (MCFC), it has some problems to be overcome such as decrease of porosity and thickness of the anode under the operating condition (at $650^{\circ}C$ and working pressure of more than 2 $kg_f/cm^2$). Recently, Ni-Al alloy anode has been proposed to replace the conventional Ni-Cr anode as an alternative material to resist a creep and inhibit the sintering. The objective of this research is to sinter the green sheet of Ni-Al alloy anode during single cell pre-treatment process, which has several advantages like cost down and simplification of manufacturing process. However, the Ni-Al alloy anode prepared with a conventional pre-treatment process showed the phase separation of Ni-Al alloy and formation of micropore(${\leqq}0.4{\mu}m$), resulting in low creep resistance and high electrolyte re-distribution. In order to prevent the Ni-Al alloy anode from phase-separating, nitrogen gas was used in the process of pre-treatment. Introducing the nitrogen, the phase separation from Ni-Al alloy into nickel and alumina was minimized and increased creep resistance. However, there was some micropore formation on the surface of Ni-Al alloy anode during the cell operation due to creation of lithium aluminate. Addition of more amount of electrolyte into a cell, especially at cathode, made the cell performance stable for 2,000 hrs. Consequently, it was possible to make the Ni-Al alloy anode with good creep resistance by the modified in-situ sintering technique.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.683-694
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• 2003

We present X-tree Diff, a change detection algorithm for tree-structured data. Our work is motivated by need to monitor massive volume of web documents and detect suspicious changes, called defacement attack on web sites. From this context, our algorithm should be very efficient in speed and use of memory space. X-tree Diff uses a special ordered labeled tree, X-tree, to represent XML/HTML documents. X-tree nodes have a special field, tMD, which stores a 128-bit hash value representing the structure and data of subtrees, so match identical subtrees form the old and new versions. During this process, X-tree Diff uses the Rule of Delaying Ambiguous Matchings, implying that it perform exact matching where a node in the old version has one-to one corrspondence with the corresponding node in the new, by delaying all the others. It drastically reduces the possibility of wrong matchings. X-tree Diff propagates such exact matchings upwards in Step 2, and obtain more matchings downwsards from roots in Step 3. In step 4, nodes to ve inserted or deleted are decided, We aldo show thst X-tree Diff runs on O(n), woere n is the number of noses in X-trees, in worst case as well as in average case, This result is even better than that of BULD Diff algorithm, which is O(n log(n)) in worst case, We experimented X-tree Diff on reat data, which are about 11,000 home pages from about 20 wev sites, instead of synthetic documets manipulated for experimented for ex[erimentation. Currently, X-treeDiff algorithm is being used in a commeercial hacking detection system, called the WIDS(Web-Document Intrusion Detection System), which is to find changes occured in registered websites, and report suspicious changes to users.

• Restorative Dentistry and Endodontics
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• v.20 no.2
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• pp.685-698
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• 1995

This study was conducted to the effect of temporary cement on the adhesiveness of dentin bonding agent to dentin surface. One hundred freshly extracted bovine mandibular incisors were grinded to expose flat labial dentin surface. The dentin surfaces were temporarized with either eugenol-containing temporary cement(TemBond and Zinc Oxide Eugenol cement) or non-eugenol temporary cement(Nogenol and TempBond NE) for 7days, and then the temporarization was removed with surgical currette and the exposed dentin surfaces were water-rinsed. Bonding specimens were made by use of All-Bond 2 and Super-Bond C&B dentin bonding agent, and stored in $37^{\circ}C$ distilled water for 24hours. The tensile bond strenth and the cohesive failure rate were measured, and then the pretreated dentin surfaces which the temporary cement had been applied to and removed from and the fractured dentin surfaces after bonding test were examined under scanning electron microscope. The results were as follows : In case of bonding with All-Bond 2, tensile bond strength of each experimental group was lower than that of the control group(p<0.05), but there was no significant difference between the bond strengths of the control group and each experimental group in case of bonding with Super-Bond C&B(p>0.05). No significant difference between tensile bond strength of experimental group, whether temporary cement contains eugenol or not, was seen(p>0.05). In case of bonding with All-Bond 2, the control group showed cohesive-adhesive mixed failure mode and the experimental groups mainly showed adhesive failure mode, but in case of bonding with Super-Bond C&B, almost of the control and the experimental groups mainly showed cohesive failure mode. On SEM examination, all of the dentin specimens pretreated with either 10 % phosphoric acid or 10% citric acid after application of the temporary cements demonstrated remnants of temporary cement attached to dentin surface.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of the Korean Institute of Gas
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• v.8 no.2 s.23
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• pp.15-27
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• 2004

The purpose of this paper is to improve the performance of Polymer electrolyte fuel cell(PEMFC) by studying the channel dimension of bipolar plates using commercial CFD program 'Fluent'. Simulations are done ranging from 0.5 to 3.0mm for different size in order to find the channel size which shoves the highst hydrogen consumption. The results showed that the smaller channel width, land width, channel depth, the higher hydrogen consumption in anode. When channel width is increased, the pressure drop in channel is decreased because total channel length Is decreased, and when land width is increased, the net hydrogen consumption is decreased because hydrogen is diffused under the land width. It is also found that the influence of hydrogen consumption is larger at different channel width than it at different land width. The change of hydrogen consumption with different channel depth isn't as large as it with different channel width, but channel depth has to be small as can as it does because it has influence on the volume of bipolar plates. however the hydrogen utilization among the channel sizes more than 1.0mm which can be machined in reality is the most at channel width 1.0, land width 1.0, channel depth 0.5mm and considered as optimum channel size. The fuel cell combined with 2cm${\times}$2cm diagonal or serpentine type flow field and MEA(Membrane Electrode Assembly) is tested using 100W PEMFC test station to confirm that the channel size studied in simulation. The results showed that diagonal and serpentine flow field have similarly high OCV and current density of diagonal (low field is higher($2-40mA/m^2$) than that of serpentine flow field under 0.6 voltage, but the current density of serpentine type has higher performance($5-10mA/m^2$) than that of diagonal flow field under 0.7-0.8 voltage.

• Laboratory Medicine Online
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• v.7 no.4
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• pp.163-169
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• 2017

Background: An automated immunohematology analyzer, DAYMATE M (DAY Medical, Switzerland), has been recently developed. The potential of this analyzer to improve test results has been evaluated. Methods: A total of 300 blood samples from Seoul St. Mary's hospital and Incheon St. Mary's hospital were tested for ABO and RhD typing. In addition, 336 antibody screening test (AST) samples and 82 patients treated with hematopoietic stem cell transplantation (HSCT) were included. AST results by DAYMATE M were compared with those obtained by a manual method using DS-Screening II (Bio-Rad Laboratories, Switzerland) and red blood cells from Selectogen (Ortho-Clinical diagnostics Inc., USA). Results: Of the 300 patients enrolled, 87, 73, 79, and 61 had type A, B, O, and AB blood, respectively. The concordance rate was 99.9% for cell typing and 97.0% for serum typing. One discordant case was classified as type B instead of AB, and six discordant serum-typing cases were type A, but classified as type AB. Among the 336 AST samples, the concordance rate was 93.2%. From 136 positive cases, six were discordant. Within the 82 HSCT-treated patients, the concordance rate for ABO blood typing was 92.2%. Among the six discordant cases, DAYMATE M typed four cases as donor type where the standard method typed them as the recipient blood type. Conclusions: The DAYMATE M automated immunohematology analyzer performs reliably for ABO and RhD typing, as well as for ASTs and on samples from patients treated with HSCT.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of the Korean Applied Science and Technology
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• v.36 no.2
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• pp.498-506
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• 2019

This study is based on a coating method that provides utilization value as a micronised powder for cosmetic raw materials using natural minerals buried in Bonghwa, Gyeongsangbuk-do in Korea. The mineral powder name is called Buseok, and chemical name is pumice powder. The results of a study on the efficacy of cosmetics are reported by the development of particulate powder to assess the performance of this powder. First of all, in order to coat the surface of this powder with oil, aluminum hydroxide was coated on the particulate surface and then coated with alkylsilan. In addition, it was coated with vegetable oil to prevent condensation of the powder and increase the dispersion in the oil phase. First; the particle size of pumice powder was from 10 to 50mm having porous holes on the surface of the particles. Second; The components of this powder contained $SiO_2$, $Al_2O_3$, $Fe_2O_3$, MgO, CaO, $K_2O_2$, $Na_2O$, $TiO_2$, $TiO_2$, MnO, $Cr_2O_3$, $V_2O_5$. Third: The particles of this powder have a planetary structure and are reddish-brown with porosity through SEM and TEM analysis. Fourth; the far-infrared radiation rate of this parabolic powder was $0.924{\mu}m$, and the radiative energy was $3.72{\times}102W/m^2$ and ${\mu}m$. In addition, the anion emission is 128 ION/cc, which shows that the coating remains unchanged. Based on these results, it is expected to be widely applied to basic cosmetics such as BB cream, cushion foundation, powderfect, and other color-coordinated cosmetics, sunblock cream, wash-off massage pack as an application of cosmetics. (Small and Medium Business Administration: S2601385)

• Journal of Life Science
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• v.29 no.5
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• pp.545-554
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• 2019

The phytochemical compounds of Pueraria, a medicinally important leguminous plant, include various isoflavones that have weak estrogenic activity and a potential role in preventing chronic disease, cancer, osteoporosis, and postmenopausal syndrome. However, the major isoflavones are derivatives of puerarin and occur mainly as unabsorbable and biologically inactive glycosides. The bioavailability of the glucosides can be increased by hydrolysis of the sugar moiety using ${\beta}$-glucosidase. In this study, we investigated the antioxidant effects of a Pueraria extract after fermentation by Lactobacillus rhamnosus BHN-LAB 76. The L. rhamnosus BHN-LAB 76 strain was inoculated into Pueraria powder and fermented at $37^{\circ}C$ for 72 hr. The total polyphenol content of the Pueraria extract increased by about 134% and the total flavonoid content increased around 110% after fermentation with L. rhamnosus BHN-LAB 76 when compared to a non-fermented Pueraria extract. Superoxide dismutase-like activities, DPPH radical scavenging, and ABTS radical scavenging increased by approximately 213%, 190%, and 107%, respectively, in the fermented Pueraria extract compared to the non-fermented Pueraria extract. Fermentation of Pueraria extracts with L. rhamnosus BHN-LAB 76 is therefore possible and can effectively increase the antioxidant effects. These results can be applied to the development of improved foods and cosmetic materials.