• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.11-22
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• 2021

Sonobuoy is widely used as a very important sensor in combat management system using P-3 patrol aircraft due to its advantages of rapid searching into wide exploration range. It is necessary to verify the performance of developed sonobuoy system using various maritime test data in order to be successfully applied in combat management system. But it is difficult to acquire various real maritime data because it needs much time and effort. Therefore we have developed in this paper a synthetic signal generator and a simulator that they can verify the performance of sonobuoy system and evaluate its operational effectiveness without conducting maritime test. We have synthesized target signals based on the characteristics of underwater sound sources, and then developed the synthesized signal generator which consider to sound propagation etc. like as underwater environment. And in the simulator development we use a HMI technique to enhance the convenience of operator, and design to verify the performance of sonobuoy system. The developed signal generator and simulator can be used as useful tools to evaluate the operational effectiveness such as optimal deployment of sonobuoy in combat management system using P-3 patrol aircraft.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.153-160
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• 2022

This study presents the bogie arrangement of the load-out system for on-ground shipbuilding. The load-out system is one of the most important systems to perform the bogie arrangement of the on-ground shipbuilding technique without dry dock facilities, and this system is composed of four pieces of equipment: bogies, driving bogie with motors, trestles, and power packs. Also, the bogie arrangement analysis (BAA) is employed to simply calculate the reaction forces at the trestle for structural safety. In this context, the purpose of this study is to propose an optimal design method to perform the bogie arrangement satisfying structural safety requirements with minimal cost. It is expected that the proposed methodology will contribute to the effective practice as well as to the improvement of competitive capability for shipbuilding companies at the on-ground shipbuilding stage. Furthermore, we describe some problems and their solutions of the deformation that may occur in the bottom of the hull during the load-out process. As a result, it is shown that we applied it to the 114K crude oil tanker (Minimum bogie 54EA) and the 174K CBM LNG carrier (Minimum bogie 88EA), it can minimize the number of bogie and critical risks (Safety rate 1.61) during the load-out of on-ground shipbuilding. Through this study, the reader will be able to learn successful load-out operation and economic shipbuilding in the future.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.181-192
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• 2022

Active vibration control system(AVCS) for helicopter enables to control the vibration generated from the main rotor and has the superb vibration reduction performance with low weight compared passive vibration reduction device. In this paper, FxLMS algorithm-based vibration control software of the light civil helicopter tansmits the control command calculated using the signals of the tachometer and accelerometers to the circular force generator(CFG) is developed and verified. According to the RTCA DO-178C/DO-331, the vibration control software is developed through the model based design technique, and real-time operation performance is evaluated in PILS(processor in-the loop simulation) and HILS(hardware in-the loop simulation) environments. In particular, the reliability of the software is improved through the LDRA-based verification coverage in the PIL environments. In order to AVCS to light civil helicopter(LCH), the dynamic response characteristic model is obtained through the ground/flight tests. AVCS configuration which exhibits the optimal performance is determined using system optimization analysis and flight test and obtain STC certification.

• Composites Research
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• v.35 no.5
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• pp.317-321
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• 2022

In this study, the PIC design method with machine learning that automatically assigning different stacking sequences according to loading types was applied bumper beam. The input value and labels of the training data for applying machine learning were defined as coordinates and loading types of reference elements that are part of the total elements, respectively. In order to compare the 2D and 3D implementation method, which are methods of representing coordinate value, training data were generated, and machine learning models were trained with each method. The 2D implementation method is divided FE model into each face and generating learning data and training machine learning models accordingly. The 3D implementation method is training one machine learning model by generating training data from the entire finite element model. The hyperparameter were tuned to optimal values through the Bayesian algorithm, and the k-NN classification method showed the highest prediction rate and AUC-ROC among the tuned models. The 3D implementation method revealed higher performance than the 2D implementation method. The loading type data predicted through the machine learning model were mapped to the finite element model and comparatively verified through FE analysis. It was found that 3D implementation PIC bumper beam was superior to 2D implementation and uni-stacking sequence composite bumper.

• Korean Journal of Food Science and Technology
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• v.53 no.4
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• pp.479-485
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• 2021

To improve the oxidative stability of Glycyrrhiza uralensis extract (GU), GU extraction conditions were optimized for maximal antioxidant activity, and GU-loaded nanocapsules were prepared by chitosan ionic gelation. The optimized ethanol concentration and extraction time were 83.0% and 32.6 min, respectively, using response surface methodology. The particle size of the GU-loaded nanocapsules ranged from 280 to 370 nm. A GU extract of 0.8 mg/mL and chitosan concentration of 2.0 mg/mL were selected as the optimal conditions for entrapment and loading efficiency. Both free GU and GU-loaded chitosan nanocapsules exhibited concentration-dependent antioxidant activity. However, the antioxidant protection factor of GU was effectively maintained when it was entrapped within the chitosan nanocapsules. In conclusion, chitosan nanoencapsulation is a potentially valuable technique for improving the oxidative stability of GU.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.497-502
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• 2022

Information delivery among young generation has a distinct tendency to prefer visual to text as means of information distribution and sharing recently, and it is natural to distribute information through Youtube or one-man broadcasting on Internet. That is, young generation usually get their information through this kind of distribution procedure. Many young generation are also drastic and more aggressive for decorating themselves very uniquely. It tends to create personal characteristics freely through drastic expression and attempt of face makeup, hair styling and fashion coordination without distinction of sex. Especially, face makeup becomes an object of major concern among males nowadays, and female of course, then it is the major means to express their personality. In this study, to meet the demands of the times, we design and implement the intellectual smart mirror application that efficiently retrieves and recommends the related videos among Youtube or one-man broadcastings produced by famous professional makeup artists to implement the face makeup congruous with our face shape, hair color & style, skin tone, fashion color & style in order to create the face makeup that represent our characteristics. We also introduce the AI technique to provide optimal solution based on the learning of user's search patterns and facial features, and finally provide the detailed makeup face images to give the chance to get the makeup skill stage by stage.