• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1095-1109
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• 2021

The technology used to recognize the location and surroundings of autonomous vehicles is called SLAM. SLAM standsfor Simultaneously Localization and Mapping and hasrecently been actively utilized in research on autonomous vehicles,starting with robotic research. Expensive GPS, INS, LiDAR, RADAR, and Wheel Odometry allow precise magnetic positioning and mapping in centimeters. However, if it can secure similar accuracy as using cheaper Cameras and GPS data, it will contribute to advancing the era of autonomous driving. In this paper, we present a method for converging monocular camera with RTK-enabled GPS data to perform RMSE 33.7 cm localization and mapping on the urban road.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.587-592
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• 2019

The importance of software technologies is becoming more prominent because of the competition to secure a competitive edge in software, which has been intensified since the emergence of smartphones and IoT. Thus, to assure the initiative in the global software industry and to foster superior human resources, there is a growing need for outstanding software development professionals. This paper analyzes the factors that affect the basic perception of software, the need for software development, and the enhancement of software coding ability based on a compulsory software class, which aims to increase the workforce of the converged software industry. The analysis shows that among other technical practices to enhance coding ability, learner-centered technical contents showed the most positive effect regarding the recognition and motive of development and are an essential factor in improving coding skills. The findings indicate that the need for program development and active involvement in the development of the program are the most important factors in improving the practical ability. The analysis presents meaningful results by suggesting a methodology for improving software development capabilities.

• Journal of Powder Materials
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• v.25 no.6
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• pp.475-481
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• 2018

Selective laser melting (SLM), a type of additive manufacturing (AM) technology, leads a global manufacturing trend by enabling the design of geometrically complex products with topology optimization for optimized performance. Using this method, three-dimensional (3D) computer-aided design (CAD) data components can be built up directly in a layer-by-layer fashion using a high-energy laser beam for the selective melting and rapid solidification of thin layers of metallic powders. Although there are considerable expectations that this novel process will overcome many traditional manufacturing process limits, some issues still exist in applying the SLM process to diverse metallic materials, particularly regarding the formation of porosity. This is a major processing-induced phenomenon, and frequently observed in almost all SLM-processed metallic components. In this study, we investigate the mechanical anisotropy of SLM-produced 316L stainless steel based on microstructural factors and highly-oriented porosity. Tensile tests are performed to investigate the microstructure and porosity effects on mechanical anisotropy in terms of both strength and ductility.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.3
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• pp.281-291
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• 2022

Global warming is accelerating due to the use of fossil fuels that have been used continuously for centuries. Now, humankind recognizes its seriousness, and is conducting research on searching for eco-friendly and sustainable energy. In the field of solar energy, which is a kind of eco-friendly and sustainable, many studies are being conducted to enhance the output performance of the module. In this study, the output improvement for the shingled module structure was studied. In order to improve the output performance of the module, the thickness of the encapsulant was increased, and the lamination process conditions have been improved accordingly. After that, the crosslinking rate was analyzed, and the suitability of the lamination process conditions was judged using this. In addition, a peeling test was conducted to analyze the correlation between the adhesion of the encapsulant and the output performance of the module. Finally, the optimization for the encapsulant material and the lamination process conditions for high-power shingled modules was established, and accordingly, the market share of high-power shingled modules in the solar module market can be expected to rise.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.3
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• pp.284-293
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• 2022

In this study, we aimed to optimize the blending conditions of sunflower oil (A), water (B), and oriental sauce (C) for canned smoked oysters Crassostrea gigas in oriental sauce (SOO). Using response surface methodology (RSM), we found that the optimal independent variables [X₁, A/(B+C); X₂, B/C] based on the salinity (Y₁) amino acid nitrogen content (Y₂), and overall acceptance (Y₃) of high-quality SOO were 48.7% (w/w) for sunflower oil, 25.5% (w/w) for water, and 25.8% (w/w) for oriental sauce. Under optimal conditions, the experimental values of Y₁, Y₂, and Y₃ were 1.68±0.4 g/100 g, 155.4±2.4 mg/100 g, 6.2±0.23 score, respectively, which were not significantly different from the predicted values (P<0.05). The SOO prepared under optimal conditions had a higher overall acceptance than commercial canned smoked oysters. These results suggest that developing canned smoked oysters in oriental sauce can be industrialized, and the product is predicted to be competitive in the global market.

• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.185-194
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• 2022

The decommission of nuclear legacy sites, which have been contaminated by previous activities such as uranium mining & milling as well as nuclear tests, has started to gain global attention. Within the Korean peninsula, Republic of Korea(ROK)has had experience in dismantling research reactors. For the Democratic People's Republic of Korea(DPRK), the possibility of nuclear activities being implemented and operations records being managed without consideration of the latest nuclear safety regulations are high. Hence, the chances of DPRK's sites remaining as nuclear legacy is also high. This study investigates approaches and considerations that needs to be taken in account in the event of a nuclear legacy site occurrence, reviewing its international cases for the solution of the legacy sites. The regulation, process of optimization, and stakeholder engagement for a nuclear legacy site should be considered in such an event. Developing legacy site response plans can be used to prevent future legacy site occurrences.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.317-318
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• 2022

In accordance with global decarbonization, optimization and productivity improvement using digital twin are being sought, and software development for optimizing ship and marine energy operation is accelerating by selecting digital twin as a future core technology. In order to reduce the operating cost of ships and strengthen the competitiveness of the shipbuilding industry due to the international strengthening of regulations on carbon emissions, it is necessary to predict the carbon emission of ships in advance and provide a carbon reduction operation solution. A plan was carried out for the development of open digital platform technology and the establishment of an environment to support the securing of carbon transparency of the ship and offshore system.