• Steel and Composite Structures
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• v.43 no.5
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• pp.523-532
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• 2022

Turbocharger technology is one of the ways to survive in a competitive market that is facing increasing demand for fuel and improving the efficiency of vehicle engines. Turbocharging allows the engine to operate at close to its maximum power, thereby reducing the relative friction losses. One way to optimally understand the behavior of a turbocharger is to better understand the heat flow. In this paper, a 1.7 liter, 4 cylinder and 16 air valve gasoline engine turbocharger with compressible, viscous and 3D flow was investigated. The purpose of this paper is numerical investigation of the number of heat transfer in gasoline engines turbochargers under 3D flow and to examine the effect of different types of coatings on its performance; To do this, modeling of snail chamber and turbine blades in CATIA and simulation in ANSYS-FLUENT software have been used to compare the results of turbine with experimental results in both adiabatic and non-adiabatic (heat transfer) conditions. It should be noted that the turbine blades are modeled using multiple rotational coordinate methods. In the experimental section, we simulated our model without coating in two states of adiabatic and non-adiabatic. Then we matched our results with the experimental results to prove the validation of the model. Comparison of numerical and experimental results showed a difference of 8-10%, which indicates the accuracy and precision of numerical results. Also, in our studies, we concluded that the highest effective power of the turbocharged engine is achieved in the adiabatic state. We also used three types of SiO₂, Sic and Si₃N₄ ceramic coatings to investigate the effect of insulating coatings on turbine shells to prevent heat transfer. The results showed that SiO₂ has better results than the other two coatings due to its lower heat transfer coefficient.

• The Korea Journal of Herbology
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• v.28 no.5
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• pp.95-101
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• 2013

Objectives : A quantitative method using high performance liquid chromatography with a photodiode array detector(HPLC-PDA) was established for the quantitative analysis of the four main compound and pattern analysis to classification Piiellia ternate, P. pedatisecta and Typhonium flagelliforme. Methods : The analytical procedure for the determination of P. ternata, together with the known main compounds uracil, uridine, guanosine and adenosine was established. Optimum HPLC-PDA separation of these P. ternata was possible on Luna C18(2) column material, using water and acetonitrile as mobile phase. The method was validated according to regulatory guidelines. In addition, this assay method were analyzed for the content of four main compound in P. ternata, P. pedatisecta and T. flagelliforme and by data obtained from the HPLC-PDA analysis was performed principal component analysis(PCA). Results : Validation results indicated that the HPLC method is well suited for the determination of the roots of P. ternata with a good linearity ($r^2$ > 0.999), precision and recovery rates. Analysis of HPLC-PDA, the average content of uracil, uridine, guanosine and adenosine was significantly higher in P. ternate>P. pedatisecta> T. flagelliforme order. The application of PCA to main compound data by HPLC-PDA permitted the effective discrimination among the three species. Conclusions : Analysis of both HPLC-PDA and PCA confirmed the fact that four main compound and pattern profiles of P. ternata, P. pedatisecta and T. flagelliforme were different from each other.

• Journal of Nutrition and Health
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• v.55 no.6
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• pp.601-616
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• 2022

In the era of the fourth industrial revolution technology, the inclusion of personalized nutrition for healthcare (PNH), when establishing a healthcare platform to prevent chronic diseases such as obesity, diabetes, cerebrovascular and cardiovascular disease, pulmonary disease, and inflammatory diseases, enhances the national competitiveness of global healthcare markets. Furthermore, since the government experienced COVID-19 and the population dead cross in 2020, as well as numerous health problems due to an increasing super-aged Korean society, there is an urgent need to secure, develop, and utilize PNH-related technologies. Three conditions are essential for the development of PNH technologies. These include the establishment of causality between obesity genome (genotype) and prevalence (phenotype) in Koreans, validation of clinical intervention research, and securing PNH-utilization technology (i.e., algorithm development, artificial intelligence-based platform, direct-to-customer [DTC]-based PNH, etc.). Therefore, a national control tower is required to establish appropriate PNH infrastructure (basic and clinical research, cultivation of PNH-related experts, etc.). The post-corona era will be aggressive in sharing data knowledge and developing related technologies, and Korea needs to actively participate in the large-scale global healthcare markets. This review provides the importance of scientific evidence based on a huge dataset, which is the primary prerequisite for the DTC obesity gene-based PNH technologies to be competitive in the healthcare market. Furthermore, based on comparing domestic and internationally approved DTC obese genes and the current status of Korean obesity genome-based PNH research, we intend to provide a direction to PNH planners (individuals and industries) for establishing scientific PNH guidelines for the prevention of obesity.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.351-357
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• 2022

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion: This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

• 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.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.2
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• pp.117-135
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• 2023

The National Archives of Korea (NAK) operates the NAK Portal as a record search system. However, user search satisfaction is too low, and the number of visitors to the portal is gradually decreasing. This study identifies the portal's issues, proposes feasible improvements, and constructs a pilot system to validate the solutions. The preliminary assessment revealed six major issues, such as poor search tool performance and the lack of consistency in search results. After clarifying the improvement measures, a pilot system was established and compared with the National Records Portal. The evaluation showed significant performance improvements in the pilot system, such as Precision, Recall, and Mean Reciprocal Rank (MRR).

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Analytical Science and Technology
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• v.37 no.2
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• pp.98-113
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• 2024

The current investigation entails the characterization of five degradation products (DPs) formed under different stress conditions of loteprednol using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In addition, this study developed a stable high-performance liquid chromatography (HPLC) method for evaluating loteprednol along with impurities. The method conditions were meticulously fine-tuned which involved the exploration of the appropriate solvent, pH, flow of the mobile phase, columns, and wavelength. The method conditions were carefully chosen to successfully resolve the impurities of loteprednol and were employed in subsequent validation procedures. The stability profile of loteprednol was exposed to stress degradation experiments conducted under five conditions, and DPs were structurally characterized by employing LC-MS/MS. The chromatographic resolution of loteprednol and its impurities along with DPs was effectively achieved using a Phenomenex Luna 250 mm C18 column using 0.1 % phosphoric acid, methanol, and acetonitrile in 45:25:30 (v/v) pumped isocratically at 0.8 mL/min with 243 nm wavelength. The method produces an accurate fit calibration curve in 50-300 ㎍/mL for loteprednol and LOQ (0.05 ㎍/mL) - 0.30 ㎍/mL for its impurities with acceptable precision, accuracy, and recovery. The stress-induced degradation study revealed the degradation of loteprednol under basic, acidic, and photolytic conditions, resulting in the formation of seven distinct DPs. The efficacy of this method was validated through LC-MS/MS, which allowed for the verification of the chemical structures of the newly generated DPs of loteprednol. This method was appropriate for assessing the impurities of loteprednol and can also be appropriate for structural and quantitative assessment of its degradation products.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.612-623
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• 2021

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

• Journal of Korean Medicine Rehabilitation
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• v.34 no.1
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• pp.1-10
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• 2024

Objectives The craniosacral therapy is closely related to the herbal meridians, so we try to explore, compare and develop pharmacopuncture that can have a synergistic effect. Methods The craniosacral pharmacopuncture, Hominis placenta pharmacopuncture, jungseongohhyeol pharmacopuncture, bamboo salt pharmacopuncture 1.8%, bamboo salt pharmacopuncture 3.0%, and normal saline, which are used with craniosacral therapy in clinical practice, were all made and prepared by ourselves. In order to compare antioxidant activity, 2,2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid), 2,2-diphenyl-1-picrylhydrazyl, ferric reducing antioxidant powder, total flavonoids, total phenolics methods were all measured. Method validation such as specificity, linearity, precision, and accuracy were performed for craniosacral pharmacopuncture. Results In all antioxidant methods, craniosacral pharmacopuncture showed the highest activity and followed by a Hominis placenta pharmacopuncture. The rest of the pharmacopunctures were measured to have low antioxidant activity. Nodakenin and glycyrrhizin were suitable as index compounds of craniosacral pharmacopuncture and they contained 0.82±0.01 ㎍/mL and 2.56±0.01 ㎍/mL respectively. Conclusions Craniosacral pharmacopuncture has the highest activity in all antioxidant activity experiments, which will help activate craniosacral therapy and quality control is possible through standardized research. Such research will contribute to the development of the oriental medicine industry.