• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.5
• /
• pp.1057-1068
• /
• 2012

According to advance on vehicle technology, many kinds of ECU(Electronic Control Unit) are equipped inside the vehicle. In-vehicle communication among ECUs is performed through CAN(Controller Area Networks). CAN have high reliability. However, it has many vulnerabilities because there is not any security mechanism for CAN. Recently, many papers proposed attacks of in-vehicle communication by using these vulnerabilities. In this paper, we propose an wireless attack model using a mobile radio communication network. We propose a secure authentication mechanism for in-vehicle network communication that assure confidentiality and integrity of data packets and also protect in-vehicle communication from the replay attack.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1209-1216
• /
• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Biomolecules & Therapeutics
• /
• v.29 no.5
• /
• pp.506-518
• /
• 2021

The imprinted tumour suppressor NOEY2 is downregulated in various cancer types, including ovarian cancers. Recent data suggest that NOEY2 plays an essential role in regulating the cell cycle, angiogenesis and autophagy in tumorigenesis. However, its detailed molecular function and mechanisms in ovarian tumours remain unclear. In this report, we initially demonstrated the inhibitory effect of NOEY2 on tumour growth by utilising a xenograft tumour model. NOEY2 attenuated the cell growth approximately fourfold and significantly reduced tumour vascularity. NOEY2 inhibited the phosphorylation of the signalling components downstream of phosphatidylinositol-3'-kinase (PI3K), including phosphoinositide-dependent protein kinase 1 (PDK-1), tuberous sclerosis complex 2 (TSC-2) and p70 ribosomal protein S6 kinase (p70S6K), during ovarian tumour progression via direct binding to vascular endothelial growth factor receptor-2 (VEGFR-2). Particularly, the N-terminal domain of NOEY2 (NOEY2-N) had a potent anti-angiogenic activity and dramatically downregulated VEGF and hypoxia-inducible factor-1α (HIF-1α), key regulators of angiogenesis. Since no X-ray or nuclear magnetic resonance structures is available for NOEY2, we constructed the three-dimensional structure of this protein via molecular modelling methods, such as homology modelling and molecular dynamic simulations. Thereby, Lys15 and Arg16 appeared as key residues in the N-terminal domain. We also found that NOEY2-N acts as a potent inhibitor of tumorigenesis and angiogenesis. These findings provide convincing evidence that NOEY2-N regulates endothelial cell function and angiogenesis by interrupting the VEGFR-2/PDK-1/GSK-3β signal transduction and thus strongly suggest that NOEY2-N might serve as a novel anti-tumour and anti-angiogenic agent against many diseases, including ovarian cancer.

• Journal of Navigation and Port Research
• /
• v.46 no.3
• /
• pp.243-250
• /
• 2022

The purpose of this study was to propose technology to detect the wave in the image in real-time, and calculate the height of the wave-overtopping through image analysis using artificial intelligence. It was confirmed that the proposed wave overtopping detection system proposed in this study could detect the occurring of wave overtopping, even in severe weather and at night in real-time. In particular, a filtering algorithm for determining if the wave overtopping event was used, to improve the accuracy of detecting the occurrence of wave overtopping, based on a convolutional neural networks to catch the wave overtopping in CCTV images in real-time. As a result, the accuracy of the wave overtopping detection through AP50 was reviewed as 59.6%, and the speed of the overtaking detection model was 70fps based on GPU, confirming that accuracy and speed are suitable for real-time wave overtopping detection.

• Journal of Animal Reproduction and Biotechnology
• /
• v.37 no.2
• /
• pp.136-143
• /
• 2022

Recent progress has been made to establish intestinal organoids for an in vitro model as a potential alternative to an in vivo system in animals. We previously reported a reliable method for the isolation of intestinal crypts from the small intestine and robust three-dimensional (3D) expansion of intestinal organoids (basal-out) in adult bovines. The present study aimed to establish next-generation intestinal organoids for practical applications in disease modeling-based host-pathogen interactions and feed efficiency measurements. In this study, we developed a rapid and convenient method for the efficient generation of intestinal organoids through the modulation of the Wnt signaling pathway and continuous apical-out intestinal organoids. Remarkably, the intestinal epithelium only takes 3-4 days to undergo CHIR (1 µM) treatment as a Wnt activator, which is much shorter than that required for spontaneous differentiation (7 days). Subsequently, we successfully established an apical-out bovine intestinal organoid culture system through suspension culture without Matrigel matrix, indicating an apical-out membrane on the surface. Collectively, these results demonstrate the efficient generation and next-generation of bovine intestinal organoids and will facilitate their potential use for various purposes, such as disease modeling, in the field of animal biotechnology.

• The Journal of Korean Medicine
• /
• v.43 no.1
• /
• pp.73-86
• /
• 2022

Objectives: In the health care industry, the influence of online reviews is growing. As medical services are provided mainly by providers, those services have been managed by hospitals and clinics. However, direct promotions of medical services by providers are legally forbidden. Due to this reason, consumers, like patients and clients, search a lot of reviews on the Internet to get any information about hospitals, treatments, prices, etc. It can be determined that online reviews indicate the quality of hospitals, and that analysis should be done for sustainable hospital marketing. Method: Using a Python-based crawler, we collected reviews, written by real patients, who had experienced Korean medicine, about more than 14,000 reviews. To extract the most representative words, reviews were divided by positive and negative; after that reviews were pre-processed to get only nouns and adjectives to get TF(Term Frequency), DF(Document Frequency), and TF-IDF(Term Frequency - Inverse Document Frequency). Finally, to get some topics about reviews, aggregations of extracted words were analyzed by using LDA(Latent Dirichlet Allocation) methods. To avoid overlap, the number of topics is set by Davis visualization. Results and Conclusions: 6 and 3 topics extracted in each positive/negative review, analyzed by LDA Topic Model. The main factors, consisting of topics were 1) Response to patients and customers. 2) Customized treatment (consultation) and management. 3) Hospital/Clinic's environments.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.213-220
• /
• 2011

In the current construction industry where various stakeholders take part, BIM Data exchange using standard format can provide a more efficient working environment for related staffs during the life-cycle of the building. Currently, the formats used to exchange the data from 3D-CAD application to structure energy analysis at the design stages are IFC, the international standard format provided by IAI, and gbXML, developed by Autodesk. However, because of insufficient data compatibility, the BIM data produced in the 3D-CAD application cannot be directly used in the energy analysis, thus there needs to be additional data entry. The reasons for this are as follows: First, an IFC file cannot contain all the data required for energy simulation. Second, architects sometimes write material names on the drawings that are not matching to those in the standard material library used in energy analysis tools. DOE-2.2 and Energy Plus are the most popular energy analysis engines. And both engines have their own material libraries. However, our investigation revealed that the two libraries are not compatible. First, the types and unit of properties were different. Second, material names used in the library and the codes of the materials were different. Furthermore, there is no material library in Korean language. Thus, by comparing the basic library of DOE-2, the most commonly used energy analysis engine worldwide, and EnergyPlus regarding construction materials; this study will analyze the material data required for energy analysis and propose a way to effectively enter these using semantic web's ontology. This study is meaningful as it enhances the objective credibility of the analysis result when analyzing the energy, and as a conceptual study on the usage of ontology in the construction industry.

• Journal of Korean Clinical Nursing Research
• /
• v.30 no.1
• /
• pp.54-64
• /
• 2024

Purpose: This descriptive survey study aimed to examine the effects of grit, social support, job crafting, and evidence-based practice competency on job satisfaction among nurses. Methods: The participants of this study were 211 clinical nurses with experience of more than six months. Data were collected using through an online survey from February 1 to February 17, 2023. The questionnaires was consisted of general characteristics, grit, social support, job crafting, evidence-based practice competency, and job satisfaction. Data were analyzed using descriptive statistics(frequency, percentage, mean, and standard deviation), t-test, ANOVA, Pearson correlation coefficient, and multiple regression analysis with the SPSS/WIN 28.0 program. Results: The average scores of the main variables were 3.08±0.44 out of four for grits, 3.67±0.52 out of five for social support, 4.20±0.64 out of five for job crafting, 4.84±0.71 out of seven for evidence-based practice competency, and 3.72±0.55 out of five for job satisfaction. In the regression model, the factors affecting the nurses' job satisfaction were grit (β=0.66, p<.001) and social support (β=0.11, p=.046), which explained 78.7% of the variance in job satisfaction. Job crafting and evidence-based practice competency were correlated with job satisfaction; however, there was no statistically significant effects of these variables on job satisfaction. Conclusion: Based on the findings of this study, grit and social support showed the most significant effects on the job satisfaction of nurses. Therefore, active support is needed to develop a strategy to improve nurses' grit and to create a supportive work environment, which would be helpful to increase their job satisfaction.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.201-220
• /
• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Therapeutic Science for Rehabilitation
• /
• v.12 no.4
• /
• pp.9-22
• /
• 2023

The Lifestyle-DEPER (Decision, Execution, Personal Factors, Environment, Resources) model explains lifestyle formation. Lifestyles are shaped through the decision, execution, and habituation stages. Factors influencing the establishment of a lifestyle are categorized as environmental, resource, and personal. The environment encompasses our surroundings and social, physical, cultural, and virtual environments. Resources refer to what individuals possess, such as health, time, economic, and social resources. Personal factors include competencies, needs, and values. At the lifestyle establishment stage, each of these factors influences a different stage. These collective processes are referred to as events, encompassing both personal and social events. Health-related lifestyle factors include physical activity, nutrition, social relationships, and occupational participation. These are the goals of lifestyle intervention. The intervention strategy based on the Lifestyle-DEPER model, called KEEP (Knowledge, Evaluation, Experience, Plan), is a comprehensive approach to promoting a healthy lifestyle by considering lifestyle formation stages and their influencing factors. This study introduces the Lifestyle-DEPER model and presents a lifestyle intervention strategy (KEEP) to promote health. Further research is required to validate the practicality of the model after applying interventions based on the lifestyle construction model.