• Journal of Wetlands Research
• /
• v.17 no.2
• /
• pp.113-117
• /
• 2015

Even though many researches are conducted for the conservation and restoration of endangered species Menyanthes trifoliata, recently, there is no study on the threatening factors to this plant. This is the first time in Korea to study growth and feeding characteristics of Deilephila elpenor as a threatening factor to Menyanthes trifoliata through an experiment. Experiment was done with 6 Eephant hawk-moth larvae and change of body weight, food preference, and ingestion amount of Bog-bean were investigated. It took 27 days from larva to pupa and maximum body weight of lavae was in the range of 4-7.5g. The food preference sequence of the lavae was Menyanthes trifoliata, Impatiens balsamina, Ampelopsis brevipedunculata var. heterophylla, Parthenocissus tricuspidata. Ingestion model shows the total amount of ingestion by a larva is 11-30g and this amount can be acquired at $0.03-0.08m^2$ of Menyanthes trifoliata pure stand. This study showed Deilephila elpenor as a potential threatening factor and suggests that the conservation and restoration plan of endangered species Menyanthes trifoliata include the control plan of Deilephila elpenor, also.

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.8
• /
• pp.134-139
• /
• 2000

In order to understand the removal mechanism and seek the optimal conditions. KrF excimer laser ablation of Cr films on glass substrates is investigated. The surface morphology of the laser-irradiated spot is examined by SEM. The measured single-shot ablation rate is found to be about two times the result of numerical analysis based on a surface vaporization model and heat conduction theory. Surface morphology examination indicates that the Cr film is removed by the sequence of melting-surface vaporization-,melt expulsion by plasma recoil and that the outmost ripple of the diffraction pattern gives a strong effect on the morphology of molten Cr during the melting and vaporization processes. To seek the optimal process parameters for micro patterning morphological investigation is carried out experimentally on samples having different chromium film thicknesses. Optimal processing conditions are determined to enhance the accuracy and quality of thin film removal for micro patterning.

• KIPS Transactions on Computer and Communication Systems
• /
• v.1 no.1
• /
• pp.11-20
• /
• 2012

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.

• Nutrition Research and Practice
• /
• v.13 no.2
• /
• pp.105-114
• /
• 2019

BACKGROUND/OBJECTIVES: Several previous studies have investigated whether regular walnut consumption positively changes heart-health-related parameters. The aim of this study was to investigate the effects of daily walnut intake on metabolic syndrome (MetS) status and other metabolic parameters among subjects with MetS. SUBJECTS/METHODS: This study was a two-arm, randomized, controlled crossover study with 16 weeks of each intervention (45 g of walnuts or iso-caloric white bread) with a 6 week washout period between interventions. Korean adults with MetS (n = 119) were randomly assigned to one of two sequences; 84 subjects completed the trial. At each clinic visit (at 0, 16, 22, and 38 weeks), MetS components, metabolic parameters including lipid profile, hemoglobin A1c (HbA1c), adiponectin, leptin, and apolipoprotein B, as well as anthropometric and bioimpedance data were obtained. RESULTS: Daily walnut consumption for 16 weeks improved MetS status, resulting in 28.6%-52.8% reversion rates for individual MetS components and 51.2% of participants with MetS at baseline reverted to a normal status after the walnut intervention. Significant improvements after walnut intake, compared to control intervention, in high-density lipoprotein cholesterol (HDL-C) (P = 0.028), fasting glucose (P = 0.013), HbA1c (P = 0.021), and adiponectin (P = 0.019) were observed after adjustment for gender, age, body mass index, and sequence using a linear mixed model. CONCLUSION: A dietary supplement of 45 g of walnuts for 16 weeks favorably changed MetS status by increasing the concentration of HDL-C and decreasing fasting glucose level. Furthermore, consuming walnuts on a daily basis changed HbA1c and circulating adiponectin levels among the subjects with MetS. This trial is registered at ClinicalTrials.gov as NCT03267901.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.3
• /
• pp.67-74
• /
• 2011

In this paper, we presented a dynamic cyber attack tree which can describe an attack scenario flexibly for an active cyber attack model could be detected complex and transformed attack method. An attack tree provides a formal and methodical route of describing the security safeguard on varying attacks against network system. The existent attack tree can describe attack scenario as using vertex, edge and composition. But an attack tree has the limitations to express complex and new attack due to the restriction of attack tree's attributes. We solved the limitations of the existent attack tree as adding an threat occurrence probability and 2 components of composition in the attributes. Firstly, we improved the flexibility to describe complex and transformed attack method, and reduced the ambiguity of attack sequence, as reinforcing composition. And we can identify the risk level of attack at each attack phase from child node to parent node as adding an threat occurrence probability.

• Journal of Korean society of Dental Hygiene
• /
• v.23 no.4
• /
• pp.209-218
• /
• 2023

Objectives: To confirm the antibacterial effects of each mouth rinse on multiple oral biofilms in vitro. Methods: The antibacterial effects of different mouth rinses were examined by ATP and counted colony forming units (CFU). Preformed oral biofilms on saliva coated hydroxyapatite (sHA) disks were treated with essential oil and saline; then, the multiple oral biofilms were observed by Scanning electron microscope (SEM). RNA sequencing analysis was performed on total RNA isolated from old biofilms of P. intermedia ATCC 49046. Results: In the CFU measured result compared to controls, preformed multiple oral biofilms were reduced from a low of 39.0% to 95.7% (p<0.05). The size of bacterial cells changed after treatment with the essential oil, and some of the cells ruptured into small pieces of cell debris. Gene expression in P. intermedia ATCC 49046 significantly altered in RNA transcribed and protein translated genes after exposure to essential oil. Conclusions: Mouth rinse solutions with different ingredients had different antibacterial effects and may alter surface structure and gene expression as determined by RNA sequencing.

• Journal of the Korea Computer Graphics Society
• /
• v.8 no.2
• /
• pp.1-7
• /
• 2002

The fast extraction of the silhouettes of a model is very useful for many applications in computer graphics and animation. In this paper, we present an efficient algorithm to compute a sequence of perspective silhouettes for a polyhedral model from a moving viewpoint. The viewpoint is assumed to move along a trajectory q(t), which is a space curve of a time parameter t. Then, we can compute the time-intervals for each edge of the model to be contained in the silhouette by two major computations: (i) intersecting q(t) with two planes and (ii) a number of dot products. If q(t) is a curve of degree n, then there are at most of n + 1 time-intervals for an edge to be in a silhouette. For each time point $t_i$ we can extract silhouette edges by searching the intervals containing $t_i$ among the computed intervals. For the efficient search, we propose two kinds of data structures for storing the intervals: an interval tree and an array. Our algorithm can be easily extended to compute the parallel silhouettes with minor modifications.

• Journal of Internet Computing and Services
• /
• v.18 no.6
• /
• pp.35-46
• /
• 2017

Recently, attackers using malicious code in cyber security have been increased by attaching malicious code to a mail and inducing the user to execute it. Especially, it is dangerous because it is easy to execute by attaching a document type file. The author analysis is a research area that is being studied in NLP (Neutral Language Process) and text mining, and it studies methods of analyzing authors by analyzing text sentences, texts, and documents in a specific language. In case of attack mail, it is created by the attacker. Therefore, by analyzing the contents of the mail and the attached document file and identifying the corresponding author, it is possible to discover more distinctive features from the normal mail and improve the detection accuracy. In this pager, we proposed IADA2(Intelligent Attack mail Detection based on Authorship Analysis) model for attack mail detection. The feature vector that can classify and detect attack mail from the features used in the existing machine learning based spam detection model and the features used in the author analysis of the document and the IADA2 detection model. We have improved the detection models of attack mails by simply detecting term features and extracted features that reflect the sequence characteristics of words by applying n-grams. Result of experiment show that the proposed method improves performance according to feature combinations, feature selection techniques, and appropriate models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.6
• /
• pp.351-361
• /
• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• Composites Research
• /
• v.36 no.1
• /
• pp.53-58
• /
• 2023

The PIC design method is assigning different stacking sequences for each shell element through the preliminary FE analysis. In previous study, machine learning was applied to the PIC design method in order to assign the region efficiently, and the training data is labeled by dividing each region into tension, compression, and shear through the preliminary FE analysis results value. However, since buckling is not considered, when buckling occurs, it can't be divided into appropriate loading type. In the present study, it was proposed PIC-NTL (PIC design using novel technique for analyzing load type) which is method for applying a novel technique for analyzing load type considering buckling to the conventional PIC design. The stress triaxiality for each ply were analyzed for buckling analysis, and the representative loading type was designated through the determined loading type within decision area divided into two regions of the same size in the thickness direction of the elements. The input value of the training data and label consisted in coordination of element and representative loading type of each decision area, respectively. A machine learning model was trained through the training data, and the hyperparameters that affect the performance of the machine learning model were tuned to optimal values through Bayesian algorithm. Among the tuned machine learning models, the SVM model showed the highest performance. Most effective stacking sequence were mapped into PIC tube based on trained SVM model. FE analysis results show the design method proposed in this study has superior external loading resistance and energy absorption compared to previous study.