• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.169-185
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• 2023

The GMT-Consortium Large Earth Finder (G-CLEF) is the first instrument for the Giant Magellan Telescope (GMT). G-CLEF is a fiber feed, optical band echelle spectrograph that is capable of extremely precise radial velocity measurement. G-CLEF Flexure Control Camera (FCC) is included as a part in G-CLEF Front End Assembly (GCFEA), which monitors the field images focused on a fiber mirror to control the flexure and the focus errors within GCFEA. FCC consists of an optical bench on which five optical components are installed. The order of the optical train is: a collimator, neutral density filters, a focus analyzer, a reimager and a detector (Andor iKon-L 936 CCD camera). The collimator consists of a triplet lens and receives the beam reflected by a fiber mirror. The neutral density filters make it possible a broad range star brightness as a target or a guide. The focus analyzer is used to measure a focus offset. The reimager focuses the beam from the collimator onto the CCD detector focal plane. The detector module includes a linear translator and a field de-rotator. We performed thermoelastic stress analysis for lenses and their mounts to confirm the physical safety of the lens materials. We also conducted the global structure analysis for various gravitational orientations to verify the image stability requirement during the operation of the telescope and the instrument. In this article, we present the opto-mechanical detailed design of G-CLEF FCC and describe the consequence of the numerical finite element analyses for the design.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Industry Promotion Research
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• v.9 no.2
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• pp.77-83
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• 2024

This study seeks to find ways to make desirable decisions in the face of ethical dilemmas experienced by social workers through research on ethical dilemmas in the social welfare administrative field. Recognizing the importance of ethical issues in the social welfare profession, we have established a social welfare code of ethics, and have opened 'Social Welfare Ethics and Philosophy' in the social welfare major curriculum to address ethical issues as a major issue. However, despite changes and desires in practice and education, the theoretical and practical interest in social welfare academia was very insufficient. Among the various ethical issues in social welfare practice, it can be said that ethical decision-making is the most burdensome for social workers. Therefore, in order to perform a role as an expert in social welfare practice, there is a need to increase awareness of the importance of ethical issues and to seek ethical methods and procedures to make the right decision by considering ethical dilemma situations. In this study, through research on various ethical dilemmas occurring in social welfare practice, it will be possible to understand ethical sensitivity in the supervision process and consider ways to train supervision experts.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• Journal of dental hygiene science
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• v.24 no.1
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• pp.37-53
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• 2024

Background: To train dental hygienists to utilize knowledge in practice, an integrated curriculum based on the competencies of dental hygienists is expanding; however, in the field of basic dental hygiene the curriculum is still fragmented and based on segmented knowledge. This study developed an integrated curriculum based on the competencies of dental hygienists in Anatomy, Histology & Embryology, Physiology, which are subjects for basic dental hygiene that have high linkage and overlap. Methods: After selecting the learning objectives for the integrated curriculum from those of Anatomy, Histology & Embryology, Physiology, the duties of the dental hygienist in relation to the learning objectives were analyzed. Learning objectives were combined with the duties of a dental hygienist to derive competencies for an integrated curriculum. Referring to the syllabus and learning objectives for each subject, the weekly educational content, learning objectives, and credits of the integrated curriculum were derived. After conducting a Delphi survey to validate the competency and content of the derived integrated curriculum, an integrated curriculum was developed. Results: By using the first and second Delphi surveys, four competencies were developed for dental hygienists that can be achieved through an integrated basic dental hygiene curriculum. In addition, an integrated curriculum including the courses Anatomy, Histology & Embryology, Physiology, Structure and Function of the Human Body/Head/Neck, and Structure and Function of the Oral Cavity was established. Conclusion: This study presents a specific example for developing a competency-based integrated curriculum that can be used as a framework to derive a competency-based integrated curriculum among subjects that can be integrated according to the linkage of learning contents and the competencies that can be achieved.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.29-34
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• 2024

The heating element utilizing the Joule heating generated when current flows through a conductor is widely researched and developed for various industrial applications such as moisture removal in automotive windshield, high-speed train windows, and solar panels. Recently, research utilizing heating elements with various nanostructures has been actively conducted to develop flexible heating elements capable of maintaining stable heating even under mechanical deformation conditions. In this study, flexible polyurethane possessing excellent flexibility was selected as the substrate, and silver (Ag) thin films with low electrical resistivity (1.6 μΩ-cm) were fabricated as the heating layer using magnetron sputtering. The 2D heating structure of the Ag thin films demonstrated excellent heating reproducibility, reaching 95% of the target temperature within 20 seconds. Furthermore, excellent heating characteristics were maintained even under mechanically deforming environments, exhibiting outstanding flexibility with less than a 3% increase in electrical resistance observed in repetitive bending tests (10,000 cycles, based on a curvature radius of 5 mm). This demonstrates that polyurethane/Ag planar heating structure bears promising potential as a flexible/wearable heating element for curved-shaped appliances and objects subjected to diverse stresses such as human body parts.