• The Korean Journal of Pain
• /
• v.35 no.2
• /
• pp.140-151
• /
• 2022

Background: Essential oils are of great interest for their analgesic and anti-inflammatory properties. We aimed to study the content of the essential oil of the Origanum vulgare of the Armenian highlands (OVA) in different periods of vegetation and to investigate its antinociceptive and anti-inflammatory effects in mice (in vivo) and cytotoxic action in cultured cells (in vitro). OVA essential oil was extracted from fresh plant material by hydro-distillation. Methods: For OVA essential oil contents determination the gas chromatography-mass spectrometry method was used. Formalin and hot plate tests and analysis of cell viability using the methyl-thiazolyl-tetrazolium (MTT) assay were used. Results: The maximal content of β-caryophyllene and β-caryophyllene oxide in OVA essential oil was revealed in the period of blossoming (8.18% and 13.36%, correspondently). In the formalin test, 4% OVA essential oil solution (3.5 mg/mouse) exerts significant antinociceptive and anti-inflammatory effects (P = 0.003). MTT assay shows approximately 60% cytotoxicity in HeLa and Vero cells for 2.0 µL/mL OVA essential oil in media. Conclusions: The wild oregano herb of Armenian highlands, harvested in the blossoming period, may be considered as a valuable source for developing pain-relieving preparations.

• Journal of Internet of Things and Convergence
• /
• v.8 no.2
• /
• pp.49-54
• /
• 2022

Immersive contents is content that provides a realistic experience by maximizing the user's five senses, and includes virtual reality, augmented reality, and mixed reality. In order to provide a sense of reality to users in immersive content, it is necessary to provide realistic visual images, hearing, and touch. However, due to the rapid change in the environment for developing immersive content, experts in training human resources are having difficulties in designing the curriculum. In this study, we propose a series of educational courses that use drones to acquire and process real-world measurement data and apply the derived data to VR, AR, and MR to help experts in training immersive content develop talent. The design of training process composes through demand survey and analysis of companies, students, and local communities. This study can be a useful resource for education experts who want to train immersive contents manpower.

• Journal of Industrial Convergence
• /
• v.20 no.4
• /
• pp.31-37
• /
• 2022

Research on what factors affect the success of the movie market is very important for reducing risks in related industries and developing the movie industry. In this study, in order to find out the degree of correlation of independent variables that affect movie performance, a survey was conducted on film experts using the AHP method and the importance of each measurement factor was evaluated. In addition, we hypothesized that factors derived from big data related to search portals and SNS will affect the success of movies due to the increase in the spread and use of smart phones. And a prediction model that reflects both the expert survey information and big data mentioned above was proposed. In order to check the accuracy of the prediction of the proposed model, it was confirmed that it was improved (10.5%) compared to the existing model as a result of verification with real data.Therefore, it is judged that the proposed model will be helpful in decision-making of film production companies and distributors.

• Communications of Mathematical Education
• /
• v.36 no.2
• /
• pp.201-227
• /
• 2022

The purpose of this study was to develop an instrument measuring mathematics anxiety suitable for Korean High school students. In order to achieve this study purpose, the study was conducted according to the procedure of setting components of mathematics anxiety, developing questions, and verifying validity and reliability. First, in order to set the components of mathematic anxiety, previous studies on mathematic anxiety. Through this, six factors of mathematic anxiety were derived. Next, new questions were developed for each of the six constituent factors. The 122 questions were revised and supplemented through two content validity tests, and the final instrument for mathematics anxiety consisted of 49 questions of 6 factors. Finally, to verify the validity and reliability of the measurement instrument for mathematics anxiety, a survey was conducted on 1,848 students from 16 universities in Seoul and the metropolitan area. Next, a validity analysis was conducted with the 1,645 responses, excluding students who answered that there was no mathematics anxiety. As a result of exploratory factor analysis, 15 out of 49 questions were removed. Six factors were named individual characteristics, pressure on achievement, abstraction in mathematics, teaching and learning style, parental attitudes, and cumulative mathematics subjects. As a result of confirmatory factor analysis, the model fit was found to be appropriate, and the convergence validity and discriminant validity were found to be good.

• The Korean Journal of Applied Statistics
• /
• v.35 no.5
• /
• pp.667-684
• /
• 2022

Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying difficulties with current gene selection methods as well as future research directions are discussed.

• Design & Manufacturing
• /
• v.16 no.1
• /
• pp.55-61
• /
• 2022

As meters become digital and smart, energy data such as electricity, gas, heat, and water can be accurately and efficiently measured with a smart meter, providing consumers with data on energy used, so that real-time demand response and energy management services can be utilized. Although it is developing from a simple metering system to a smart metering industry to create a high value-added industry fused with ICT, illegal counterfeiting of electronic meters is causing problems in intelligent crimes such as manipulation and hacking of SW. The meter not only allows forgery of the meter data through arbitrary manipulation of the SW, but also leaves a fatal error in the metering performance, so that the OIML requires the validation of the SW from the authorized institution. In order to solve this problem, a quantitative confirmation device was developed in order to eradicate the act of cheating the fuel oil quantity through encoder pulse operation and program modulation, etc. In order to prevent the act of deceiving the lubricator, a device capable of checking pulse forgery was developed, manufactured, and verified. In addition, the performance of the device was verified by conducting an experiment on the meter being used in the actual field. It is judged that the developed quantitative confirmation device can be applied to other flow meters other than lubricators, and in this case, accurate measurement can be induced.

• Communications of Mathematical Education
• /
• v.36 no.4
• /
• pp.589-610
• /
• 2022

This study aimed to derive pedagogical implications by comparing and analyzing how the concept of pi is taught in 10 different elementary mathematics textbooks, which are scheduled to be applied from 2023. We developed a textbook analysis framework by previous studies on the concept of pi and the teaching of pi, and analyzed in terms of three instructional elements (i.e. inferring conceptsof pi, understanding properties of pi, and applying relationships). We derived the need to emphasize various contexts for estimation of pi, presentation of problem situations that provide motivation to actually measure diameters and circumferences, providing an opportunity to explore the properties of measurement, and an experience the flexibility of selecting an approximate value of pi. Based on the above conclusions and pedagogical implications through the research results., we suggested ways to teach the concept of pi in elementary mathematics and improvement points for developing textbooks focusing on the context of introduction of pi and the use of technological tools.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1935-1941
• /
• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2004.03a
• /
• pp.802-806
• /
• 2004

For the protection of the local air quality and the global atmosphere, the emissions of trace species including nitric oxides (NO and NO$_2$) from gas turbines are regulated by local governments and by the International Civil Aviation Organization. In-situ measurements of such species are needed not only for the development of advanced low-emission combustion concepts but also for providing emissions data required for the sound assessment of the effects of the emissions on environment. We have been developing a laser absorption system that has a capability of simultaneous determination of NO and NO$_2$concentrations in the exhaust jets from aero gas turbines. A diode laser operating near 1.8 micrometer is used for the detection of NO while a separated visible tunable diode laser operating near 676 nanometers is used for NO$_2$. The sensitivities at elevated temperature conditions were determined for simulated gas mixtures heated up to 500K in a heated cell of a straight 0.5 m optical path. Sensitivity limits estimated as were 30 ppmv-m and 3.7 ppmv-m for NO and NO$_2$, respectively, at a typical exhaust gas temperature of 800K. Experiments using the simulated exhaust flows have proven that $CO_2$ and $H_2O$ vapor - both major combustion products - do not show any interference in the NO or NO$_2$ measurements. The measurement system has been applied to the NO/NO$_2$ measurements in NO and NO$_2$ doped real combustion gas jets issuing from a rectangular nozzle having 0.4 m optical path. The lower detection limits of the system were considerably decreased by using a multipass optical cell. A pair of off-axis parabola mirrors successfully suppressed the beam steering in the combustion gas jets by centralizing the fluctuating beam in sensor area of the detectors.

• Journal of the Society of Disaster Information
• /
• v.18 no.4
• /
• pp.847-860
• /
• 2022

Purpose: The purpose of this paper is to present a basic study on the development of a self-generation infrastructure for monitoring the health of harbour structures. Method: By developing a self-generation system and fiber optic sensors for seawater, the study provides basic research data on port structure health monitoring. Result: Through sunlight simulation analysis, 4-5 hours of sunlight can be secure in the domestic environment. Through this, the optical splitter (Introgate) that collects the raw data from the FBG sensor applicable to seawater, the MCU that calculates it, the IoT module with wireless communication functionality, the monitoring server and the supply system are set up. Conclusion: Monitoring port structures directly with fiber optic probes (FBG) and the possibility of using selfpowered systems were confirmed.