• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.21-29
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• 2024

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the "drone" keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI's power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.33-53
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• 2024

This paper presents a comprehensive investigation into the optimization of Flight Management Systems (FMS) with a particular emphasis on data processing efficiency by conducting a comparative study with conventional methods to edge-computing technology. The objective of this research is twofold. Firstly, it evaluates the performance of FMS navigation systems using conventional and edge computing methodologies. Secondly, it aims to extend the boundaries of knowledge in edge-computing technology by conducting a rigorous analysis of terrain data and its implications on flight path optimization along with communication with ground stations. The study employs a combination of simulation-based experimentation and algorithmic computations. Through strategic intervals along the flight path, critical parameters such as distance, altitude profiles, and flight path angles are dynamically assessed. Additionally, edge computing techniques enhance data processing speeds, ensuring adaptability to various scenarios. This paper challenges existing paradigms in flight management and opens avenues for further research in integrating edge computing within aviation technology. The findings presented herein carry significant implications for the aviation industry, ranging from improved operational efficiency to heightened safety measures.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.79-100
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• 2018

Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summarization techniques by surveying recent evaluation methodologies. At the end of the survey, we emphasize on both current and future research challenges in this domain through a level of depth analysis of the most recent summarization approaches.

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.324-329
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• 2014

Purpose: The finite element method (FEM) is advantageous because it can save time and cost by reducing the number of samples and experiments in the effort to identify design factors. In computational problem-solving it is necessary that the exact material properties are input for achieving a reliable analysis. However, in the case of fiber boards, it is difficult to measure their cross-directional material properties because of their small thickness. In previous research studies, the Poisson's ratio was measured by analyzing ultrasonic wave velocities. Recently, the Poisson's ratio was measured using a high-speed digital camera. In this study, we measured the transverse strain of a fiber board and calculated its Poisson's ratio using a high-speed digital camera in order to apply these estimates to a FEM analysis of a fiber board, a corrugated board, and a corrugated box. Methods: Three different fiber board samples were used in a uniaxial tensile test. The longitudinal strain was measured using the Universal Testing Machine. The transverse strain was measured using an image processing method. To calculate the transverse strain, we acquired images of the fiber board before the test onset and before the fracture occurred. Acquired images were processed using the image processing program MATLAB. After the images were converted from color to binary, we calculated the width of the fiber board. Results: The calculated Poisson's ratio ranged between 0.2968-0.4425 (Machine direction, MD) and 0.1619-0.1751 (Cross machine direction, CD). Conclusions: This study demonstrates that measurement of the transverse properties of a fiber board is possible using image processing methods. Correspondingly, these processing methods could be used to measure material properties that are difficult to measure using conventional measuring methodologies that employ strain gauge extensometers.

• Journal of Preventive Medicine and Public Health
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• v.49 no.2
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• pp.129-133
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• 2016

Objectives: The systematic review (SR) is a research methodology that aims to synthesize related evidence. Updating previously conducted SRs is necessary when new evidence has been produced, but no consensus has yet emerged on the appropriate update methodology. The authors have developed a new SR update method called 'adaptive meta-analysis' (AMA) using the 'cited by', 'similar articles', and 'related articles' citation discovery tools in the PubMed and Scopus databases. This study evaluates the usefulness of these citation discovery tools for updating SRs. Methods: Lists were constructed by applying the citation discovery tools in the two databases to the articles analyzed by a published SR. The degree of overlap between the lists and distribution of excluded results were evaluated. Results: The articles ultimately selected for the SR update meta-analysis were found in the lists obtained from the 'cited by' and 'similar' tools in PubMed. Most of the selected articles appeared in both the 'cited by' lists in Scopus and PubMed. The Scopus 'related' tool did not identify the appropriate articles. Conclusions: The AMA, which involves using both citation discovery tools in PubMed, and optionally, the 'related' tool in Scopus, was found to be useful for updating an SR.

• Particle and aerosol research
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• v.20 no.1
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• pp.1-11
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• 2024

Children are vulnerable to bad indoor air quality, and many researches on indoor air quality have been done with various methodologies. Herein, we used the genetic algorithm, one of the optimization methods, for the analysis based on better estimation values that are not easy to measure. A children playroom and a Taekwondo gym were chosen for the different degree of physical activity. After estimation of the number of occupants, the generation degree of CO2 and PM2.5 were determined from the data of the indoor air quality monitors. Relative errors were below 1% for all cases. Due to many air-treating electronics, the PM2.5 in the children playroom was well-managed compared to that in the Taekwondo gym. The PM2.5-generating activities were calculated and that of the Taekwondo gym was higher than that of the children playroom. The PM2.5 generating values were on the positive relationship with CO2 generating values. This means that we can obtain meaningful information from limited measurement data. For the numerous children facilities, indoor air quality can be easily analyzed and this might contribute to enhancing the children health.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Journal of Korean Society on Water Environment
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• v.34 no.4
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• pp.391-398
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• 2018

The main purpose of this study is to suggest and recommend the more reliable flow direction methods within the framework of DEM and power law distribution, by investigating the existing methodologies. To this end SFD (single flow direction method), MFD (multiple flow direction method) and IFD (Infinite flow direction method) are applied to analyze the determination of a flow direction for the water particles as seen in the Jeonjeokbigyo basin, and then assessed with respect to the variation of flow accumulation in that region. As the main results revealed, the study showed the different patterns of flow accumulation are found out from each applications of flow direction methods utilized in this study. This brings us to understand that as the flow dispersion on DEM increases, in this case the contributing areas to the outlet grow in sequence of SFD, IFD, MFD, but it is noted that the contribution of individual pixels into outlet decreases at that time. In what follows, especially with the MFD and IFD, the result tends to make additional hydrologic abstraction from rainfall excess, as noted due to the flow dispersion within flow paths on DEM. Based on the parameter estimation for a power law distribution, which is frequently used for identify the aggregation structure of complex system, by maximum likelihood flow accumulation can be thought of as a scale invariance factor. In this regard, the combination of flow direction methods could give rise to the more realistic water flow on DEM, as revealed through the separate flow direction methods as utilized for dispersion and aggregation effects of water flow within the available different topographies.

• Korean Chemical Engineering Research
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• v.49 no.5
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• pp.505-509
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• 2011

Visible light-activated photocatalysts (based on doped titania) are the subject of intensive current research due to the promise they offer in relation to solar powered systems for photocatalysis, hybrid systems for $CO_2$ conversion and hydrogen production from water. Current synthetic methodologies suffer from one or more serious shortcomings, which seriously hinder practical application. These include high cost, irreproducibility, difficulty in controlling the dopant level and unsuitability for scale up. In this review new reproducible and controllable methods (developed by Lambert group, Cambridge University) allowing the synthesis of practical quantities of efficient, visible light active anion (e.g. N, C and B) doped $TiO_2$ photocatalysts are summarized.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.390-400
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• 2022

Military deception is an action executed to deliberately mislead enemy's decision by deceiving friendly forces intention. In the lessons learned from war history, deception appears to be a critical factor in the battlefield for successful operations. As training using war-game simulation is growing more important, it is become necessary to implement military deception in war-game model. However, there is no logics or rules proven to be effective for CGF(Computer Generated Forces) to conduct deception behavior automatically. In this study, we investigate methodologies for CGF to learn and conduct military deception using Reinforcement Learning. The key idea of the research is to define a new criterion called a "deception index" which defines how agent learn the action of deception considering both their own combat objectives and deception objectives. We choose Korea Marine Corps Amphibious Demonstrations to show applicability of our methods. The study has an unique contribution as the first research that describes method of implementing deception behavior.