• Journal of Ginseng Research
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• v.33 no.4
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• pp.316-323
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• 2009

Ginseng saponins have various pharmacological effects on the immune system. 20(S)-protopanaxadiol (PPD) and 20(S)-protopanaxatriol (PPT) are the species of ginseng saponin metabolites that are formed by human intestinal bacteria and detected in circulation. The effects of PPD and PPT on the inflammatory mediator release from the activated mast cells were tested. Histamine release was evaluated in activated guinea pig lung mast cells, and the secretion of interleukin-4 (IL-4), interleukin-8 (IL-8), and the tumor necrosis factor-${\alpha}$ (TNF-${\alpha}$) was assessed in an HMC-1 cell after treating it with ginseng saponin metabolites. The results are as follows. PPT, at its maximum concentration of $100\;{\mu}M$, completely abolished the secretion of IL-4 from the PMA-stimulated HMC-1 cell. It also inhibited IL-8 secretion from the same cells by about 40-50% of the PMA-treated DMSO control. PPD, at its maximum concentration of $100\;{\mu}M$, showed a tendency to induce histamine release from the guinea pig lung mast cells. It inhibited the secretion of IL-4 (by 89% of the PMA-treated DMSO control) in the PMA-stimulated HMC-1 cell, but did have a significant effect on the IL-8 release from the same cell. Both PPD and PPT showed no effects, however, on the release of TNF-${\alpha}$ from the PMA-stimulated HMC-1 cell. These results suggest that PPD and PPT are from the ginseng metabolites that are responsible for the immunomodulating activity of ginseng extracts when they are taken orally.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.5
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• pp.285-291
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• 2014

The statistical information of the coastal water temperature monitoring data can be biased because of outliers and missing intervals. Though a number of outlier detection methods have been developed, their applications are very limited to the in-situ monitoring data because of the assumptions of the a prior information of the outliers and no-missing condition, and the excessive computational time for some methods. In this study, the practical robust method is developed that can be efficiently and effectively detect the outliers in case of the big-data. This model is composed of these two parts, one part is the construction part of the approximate components of the monitoring data using the robust smoothing and data re-sampling method, and the other part is the main iterative outlier detection part using the detailed components of the data estimated by the approximate components. This model is tested using the two-years 5-minute interval water temperature data in Lake Saemangeum. It can be estimated that the outlier proportion of the data is about 1.6-3.7%. It shows that most of the outliers in the data are detected and removed with satisfaction by the model. In order to effectively detect and remove the outliers, the outlier detection using the long-span smoothing should be applied earlier than that using the short-span smoothing.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.53-61
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• 2014

Landsat OLI satellite imagery can be applied to various remote sensing applications, such as generation of land cover map, urban area analysis, extraction of vegetation index and change detection, because it includes various multispectral bands. In addition, land cover map is an important information to monitor and analyze land cover using GIS. In this paper, land cover map is generated by using Landsat OLI and existing land cover map. First, training dataset is obtained using correlation between existing land cover map and unsupervised classification result by K-means, automatically. And then, spectral signatures corresponding to each class are determined based on training data. Finally, abundance map and land cover map are generated by using iterative spectral mixture model. The experiment is accomplished by Landsat OLI of Cheongju area. It shows that result by our method can produce land cover map without manual training dataset, compared to existing land cover map and result by supervised classification result by SVM, quantitatively and visually.

• Korean journal of applied entomology
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• v.36 no.4
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• pp.323-330
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• 1997

The apple leafminer, Phyllonorycrer ringoniella (Lepidoptera: Gracillariidae), is the most important pest on apple leaves and is not easy to be controlled with insecticides because of leafmining habit of its larvae. This study was undertaken to develop an artificial diet for mass rearing of the moth, and to look at their reproductive behavior. For developing an artificial diet the problem of diet contamination with microorganisms and how to supply artificial diet to first instar larvae have to be solved first. Artificial oviposition trial was successful with shadowy bottomside of various papers sprayed with apple leaf extracts. The highest activity of both calling and mating was observed within 30 min. after the onset of photophase and 3- to 4-day -old adults showed the highest mating rate. A ring-shaped sex pheromone gland was found at the intersegmental membrane between the 8th and 9th abdominal segments of the female. (E, 2)-4,lOtetradecadienyl acetate was identified as the major component from the femall gland extracts by the gas chromatography (GC) and GC-mass spectrometry. Additionally it was thought that (a-10-tetradecenyl acetate could be the second component of their sex pheromone.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.35-43
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• 2009

In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.

• Journal of the Korean Geosynthetics Society
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• v.18 no.4
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• pp.299-306
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• 2019

In this study, a model experiment that simulates the behaviour of the ground composed of several compacted layer was intended to measure the loosened area in the event of a ground cavity through a LAD (Loosened area detector). It was confirmed that the size of the cone diameter was affected by the ground composed of fine grain + granulated soil layered through the model soil. In order to select the appropriate cone type, a scale effect experiment was conducted. From the test results, a micro-cone was chosen for the most suitable indoor model experiment. In the case of applying LAD in this study, the loosening condition of the ground was determined by the rapid change in penetration resistance caused by the difference in the boundary surface and relative density due to the compaction of the ground for indoor model testing. The range of loosened area occurring in the cavity was estimated through the penetration resistance characteristics on the ground, and the failure area was identified through the reduction rate of penetration resistance in the loosening area.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.71-83
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• 2017

In recent years, by utilizing the greatest strengths of process mining, the various research activities have been actively progressed to use auditing work of business organization. On the other hand, there is insufficient research on systematic and efficient analysis of massive data generated under big data environment using process mining, and proactive monitoring of risk management from audit side, which is one of important management activities of corporate organization. In this study, we intend to realize Hadoop-based internal audit integrated real-time monitoring system in order to detect the abnormal symptoms in prevent accidents in advance. Through the integrated real-time monitoring system for purchasing audit, we intend to realize strengthen the delivery management of purchasing materials ordered, reduce cost of purchase, manage competitive companies, prevent fraud, comply with regulations, and adhere to internal control accounting system. As a result, we can provide information that can be immediately executed due to enhanced purchase audit integrated real-time monitoring by analyzing data efficiently using process mining via Hadoop-based systems. From an integrated viewpoint, it is possible to manage the business status, by processing a large amount of work at a high speed faster than the continuous monitoring, the effectiveness of the quality improvement of the purchase audit and the innovation of the purchase process appears.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.541-548
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• 2006

Sensor network is a network for realizing the ubiquitous computing circumstances, which aggregates data by means of observation or detection deployed at the inaccessible places with the capacities of sensing and communication. To realize this circumstance, data which sensor nodes gathered from sensor networks are delivered to users, in which it is required to encrypt the data for the guarantee of secure communications. Therefore, it is needed to design key management scheme for encoding appropriate to the sensor nodes which feature continual data transfer, limited capacity of computation and storage and battery usage. We propose a key management scheme which is appropriate to sensor networks organizing hierarchical architecture. Because sensor nodes send data to their parent node, we can reduce routing energy. We assume that sensor nodes have different security levels by their levels in hierarchy. Our key management scheme provides different key establishment protocols according to the security levels of the sensor nodes. We reduce the number of sensor nodes which share the same key for encryption so that we reduce the damage by key exposure. Also, we propose key update protocols which take different terms for each level to update established keys efficiently for secure data encoding.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.317-325
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• 2020

The development of a technique that can monitor the cracks, which is one of the typical types of damage, is necessary to secure the structural safety of elastically restrained cantilever-type beams with a tip mass that is used widely in infrastructure. Impedance techniques have been actively researched to detect cracks, and the cracks were estimated mainly by experimentally investigating the relationship between the crack and impedance signal. This study examined the correlation between the change in the impedance signals due to the crack, and the natural frequency obtained analytically. After updating the analysis model for the intact beam, the impedance signal was measured while gradually inflicting cracks in the cantilever-type beam, and the damage index was obtained. The results were compared with the natural frequencies calculated from the updated analysis model to investigate the correlation. A close correlation was observed between the experimentally obtained impedance damage index, and the analytically calculated natural frequency. Using this correlation, the structural characteristics could be evaluated more accurately from the damage estimation results, and the behavior of the structure could be predicted effectively using the analysis model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.