• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.27-31
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• 2006

GNSS Services and Applications are today in permanent evolution in all the market sectors. This evolution comprises: ${\bullet}$ New constellations and systems, being GALILEO probably the most relevant example, but not the only one, as other regions of the world also dwell into developing their own elements (e.g. the Chinese Beidou system). ${\bullet}$ Modernisation of existing systems, as is the case of GPS and GLONASS ${\bullet}$ New Augmentation services, WAAS, EGNOS, MSAS, GRAS, GAGAN, and many initiatives from other regions of the world ${\bullet}$ Safety of Life services based on the provision of integrity and reliability of the navigation solutions through SBAS and GBAS systems, for aeronautical or maritime applications ${\bullet}$ New Professional applications, based on the unprecedented accuracies and integrity of the positioning and timing solutions of the new navigation systems with examples in science (geodesy, geophysics), Civil engineering (surveying, construction works), Transportation (fleet management, road tolling) and many others. ${\bullet}$ New Mass-market applications based on cheap and simple GNSS receivers providing accurate (meterlevel) solutions for daily personal navigation and information needs. Being on top of this evolving market requires an active participation on the key elements that drive the GNSS development. Early access to the new GNSS signals and services and appropriate testing facilities are critical to be able to reach a good market position in time before the next evolution, and this is usually accessible only to the large system developers as the US, Europe or Japan. Jumping into this league of GNSS developers requires a large investment and a significant development of technology, which may not be at range for all regions of the world. Bearing in mind this situation, MAGIC appears as a concept initiated by a small region within Europe with the purpose of fostering and supporting the development of advanced applications for the new services that can be enabled by the advent of SBAS systems and GALILEO. MAGIC is a low cost platform based on the application of technology developed within the EGNOS project (the SBAS system in Europe), which encompasses the capacity of providing real time EGNOS and, in the near future, GALILEO-like integrity services. MAGIC is designed to be a testing platform for safety of life and liability critical applications, as well as a provider of operational services for the transport or professional sectors in its region of application. This paper will present in detail the MAGIC concept, the status of development of the system within the Madrid region in Spain, the results of the first on-field demonstrations and the immediate plans for deployment and expansion into a complete SBAS+GALILEO regional augmentation system.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.325-331
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• 2022

As IoT devices are used in various ways in an edge network environment, multiple studies are being conducted that utilizes the information collected from IoT devices in various applications. However, it is not easy to apply accurate IoT data immediately as IoT data collected according to network environment (interference, interference, etc.) are frequently missed or error occurs. In order to minimize mistakes in IoT data collected in an edge network environment, this paper proposes a management technique that ensures the reliability of IoT data by randomly generating signature values of IoT data and allocating only Security Information (SI) values to IoT data in bit form. The proposed technique binds IoT data into a blockchain by applying multiple hash chains to asymmetrically link and process data collected from IoT devices. In this case, the blockchainized IoT data uses a probability function to which a weight is applied according to a correlation index based on deep learning. In addition, the proposed technique can expand and operate grouped IoT data into an n-layer structure to lower the integrity and processing cost of IoT data.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• Composites Research
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• v.36 no.3
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• pp.167-172
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• 2023

The manufacturing technology of composite material is applicable with filler characteristics maintaining low cost, flexibility, and easy process to develope the various functional composite materials. To realize functional composites, various researches on the high performance of composite materials using graphene as a filler is being actively conducted. In this study, physical and chemical properties were investigated using graphene to improve high functional properties. Graphene oxide (GO) was prepared using graphane nanoplatelet (GNP), and reduced graphene oxide (R-GO) was formed by reducing GO. The physical properties of GO and R-GO were analyzed, and the reliability of the manufactured method was reviewed by comparing that of GNP results. As a result of analysis by Raman spectroscopy, in the case of R-GO, it was confirmed that the intensity of D-peak and G-peak decreased compared to GO, and an increase of 0.08 was observed through the ratio of ID/IG. For the FTIR results, GO and RGO has a repeating C-C and C=C connection structure unlike GNP. GO and R-GO show clear peaks for C-O bond, C=C bond, C=O bond, and O-H bonding. As a result of X-ray diffraction analysis, GNP showed a wide diffraction peak at 25.86° of (002) plane characteristics, whereas GO and R-GO showed peaks corresponding to (001) and (100) planes. It was also found that the interlayer distance of GO increased by about 2.6 times compared to GNP.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.1
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• pp.1-7
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• 2024

β-Ga₂O₃ is a material with a wide band gap of ~4.8 eV and a high breakdown-voltage of 8 MV/cm, and is attracting much attention in the field of power device applications. In addition, compared to representative WBG semiconductor materials such as SiC, GaN and Diamond, it has the advantage of enabling single crystal growth with high growth rate and low manufacturing cost [1-4]. In this study, we succeeded in growing a 10 mm thick β-Ga₂O₃ single crystal doped with 0.3 mol% SnO₂ through the EFG (Edge-defined Film-fed Growth) method using multi-slit structure. The growth direction and growth plane were set to [010]/(010), respectively, and the growth speed was about 12 mm/h. The grown β-Ga₂O₃ single crystal was cut into various crystal planes (010, 001, 100, ${\bar{2}}01$) and surface processed. The processed samples were compared for characteristics according to crystal plane through analysis such as XRD, UV/VIS/NIR/Spec., Mercury Probe, AFM and Etching. This research is expected to contribute to the development of power semiconductor technology in high-voltage and high-temperature applications, and selecting a substrate with better characteristics will play an important role in improving device performance and reliability.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• The Journal of the Acoustical Society of Korea
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• v.26 no.7
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• pp.361-365
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• 2007

Aero-pulsation noise, generally caused by geometric asymmetry of a rotating device, is one of considerable sources of annoyance in passenger cars using the turbocharged diesel engine. Main source of this noise is the compressor wheel in the turbocharger system, and can be reduced by after-treatment devices such as silencers, but which may increase the manufacturing cost. More effective solution is to improve the geometric symmetry over all, or to control the quality of components by sorting out inferior ones. The latter is more simple and reasonable than the former in view of manufacturing. Thus, an appropriate discrimination method should be needed to evaluate aero-pulsation noise level at the production line. In this paper, we introduce the accurate method which can measure the noise level of aero-pulsation and also present its evaluation criteria. Besides verifying the reliability of a measurement system - a rig test system-, we analyze the correlation between the results from rig tests and those from vehicle tests. The gage R&R method is carried out to check the repeatability of measurements over 25 samples. From the result, we propose the standard specification which can discriminate inferior products from superior ones on the basis of aero-pulsation noise level.

• Journal of the Korea Society for Simulation
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• v.33 no.2
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• pp.1-11
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• 2024

A cyber-physical system is a technology that connects the physical systems of a manufacturing environment with a cyber space to enable simulation. One of the major challenges in this technology is the seamless communication between these two environments. In complex manufacturing processes, it is crucial to adapt to various protocols of manufacturing equipment and ensure the transmission and reception of a large volume of data without delays or errors. In this study, we propose a method for constructing agent models for real-time simulation-capable cyberphysical systems. To achieve this, we design data collection units as independent agent models and effectively integrate them with existing simulation tools to develop the overall system architecture. To validate the proposed structure and ensure reliability, we conducted empirical testing by integrating various equipment from a real-world smart microfactory system to assess the data collection capabilities. The experiments involved testing data delay and data gaps related to data collection cycles. As a result, the proposed approach demonstrates flexibility by enabling the application of various internal data collection methods and accommodating different data formats and communication protocols for various equipment with relatively low communication delays. Consequently, it is expected that this approach will promote innovation in the manufacturing industry, enhance production line efficiency, and contribute to cost savings in maintenance.