• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.382-393
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• 2010

Thermal mixing by steam jets in a pool is dominantly influenced by a turbulent water jet generated by the condensing steam jets, and the proper prediction of this turbulent jet behavior is critical for the pool mixing analysis. A turbulent jet flow induced by a steam jet discharged through a vertical upward single hole into a subcooled water pool was subjected to computational fluid dynamics (CFD) analysis. Based on the small-scale test data derived under a horizontal steam discharging condition, this analysis was performed to validate a CFD method of analysis previously developed for condensing jet-induced pool mixing phenomena. In previous validation work, the CFD results and the test data for a limited range of radial and axial directions were compared in terms of profiles of the turbulent jet velocity and temperature. Furthermore, the behavior of the turbulent jet induced by the steam jet through a horizontal single hole in a subcooled water pool failed to show the exact axisymmetric flow pattern with regards to an overall pool mixing, whereas the CFD analysis was done with an axisymmetric grid model. Therefore, another new small-scale test was conducted under a vertical upward steam discharging condition. The purpose of this test was to generate the velocity and temperature profiles of the turbulent jet by expanding the measurement ranges from the jet center to a location at about 5% of $U_m$ and 10 cm to 30 cm from the exit of the discharge nozzle. The results of the new CFD analysis show that the recommended CFD model of the high turbulent intensity of 40% for the turbulent jet and the fine mesh grid model can accurately predict the test results within an error rate of about 10%. In this work, the turbulent jet model, which is used to simply predict the temperature and velocity profiles along the axial and radial directions by means of the empirical correlations and Tollmien's theory was improved on the basis of the new test data. The results validate the CFD model of analysis. Furthermore, the turbulent jet model developed in this study can be used to analyze pool thermal mixing when an ellipsoidal steam jet is discharged under a high steam mass flux in a subcooled water pool.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.295-311
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• 2021

In this study, an experiment was carried out on the field applicability of tension measuring devices of the cables in cable-stayed bridges. The vibration method was used to estimate the tension of cables of cable-stayed bridge, and the mode characteristics of the cable were analyzed using a cable tension measuring device. GTDL360, NI Module, and 9 Axes Motion Sensorwere applied to estimate the cable tension of five target bridges. Numerical analysis of the five target bridges was conducted to analyze the natural frequency of the cable and cable tension. The estimated tension of the cable based on field measurements and estimated tension of cable by numerical analysis were compared with the estimated tension of the cable based on field measurements. The analysis showed that the measured tension of the cable based on field measurements was within the margin of error. Therefore, it is safe to apply these measuring devices to the site. As a result of comparing and analyzing the values of the acceleration-based cable estimation tension and numerical analysis of the field demonstration bridge, the acceleration-based cable estimation of tension is deemed appropriate within the allowable range. On-site applicability analysis revealed limitations of the measuring devices, such as the installation location of sensors and weather conditions, so continuous follow-up research on smart cable tension measuring systems is expected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.209-218
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• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.141-148
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• 2021

In some intersections or busy traffic roads, there are more pedestrians in a specific period of time, and there are many traffic accidents caused by road congestion. Especially at the intersection where there are schools nearby, it is particularly important to protect the traffic safety of students in busy hours. In the past, when designing traffic lights, the safety of pedestrians was seldom taken into account, and the identification of motor vehicles and traffic optimization were mostly studied. How to keep the road smooth as far as possible under the premise of ensuring the safety of pedestrians, especially students, will be the key research direction of this paper. This paper will focus on person, motorcycle, bicycle, car and bus recognition research. Through investigation and comparison, this paper proposes to use YOLO v4 network to identify the location and quantity of objects. YOLO v4 has the characteristics of strong ability of small target recognition, high precision and fast processing speed, and sets the data acquisition object to train and test the image set. Using the statistics of the accuracy rate, error rate and omission rate of the target in the video, the network trained in this paper can accurately and effectively identify persons, motorcycles, bicycles, cars and buses in the moving images.

• Journal of the Korean GEO-environmental Society
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• v.12 no.11
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• pp.71-77
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• 2011

Recently, non-destructive stress measurement method using magnetic anisotropy sensor has been applied to the construction site such as steel bridges and steel pipes. In addition, steel rib used in the tunnel construction site was found to be possible to measure the stress by non-destructive method. In this study, steel loading experiments using magnetic anisotropy sensor developed in Japan and strain gauges were conducted to derive stress sensitivity curve for domestic steel SS400. Also, additional steel loading experiments and numerical analysis were performed for evaluation of applicability for non-destructive stress measurement method using magnetic anisotropy sensor. As a result of this study, stress sensitivity curves for domestic steel SS400 were derived using output voltage measured by magnetic anisotropy sensor and average of stress measured by strain gauges depending on the measurement location. And as a result of comparing additional steel loading experiments with the numerical analysis, error level of magnetic anisotropy sensor is around 20MPa. When considering the level of the yield stress(245MPa) of steel, in case of using magnetic anisotropy sensor in order to determine the stress status of steel, it has sufficient accuracy in engineering. Especially, magnetic anisotropy sensor can easily identify the current state of stress which considers residual stress at steel structure that stress measurement sensor is not installed, so we found that magnetic anisotropy sensor can be applied at maintenance of steel structure conveniently.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.59-69
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• 2021

Recently, large-scale offshore and coastal structures have been constructed owing to the increasing interest in eco-friendly energy development. To achieve this, precise simulations of waves are necessary to ensure the safe operations of marine structures. Several experiments are required in the field to understand the offshore wave; however, in terms of scale, it is difficult to control variables, and the cost is significant. In this study, numerical waves under various wave conditions are produced using a piston-type wavemaker, and the produced wave profiles are verified by comparing with the results from a numerical wave tank (NWT) modeled using the smoothed particle hydrodynamics (SPH) method and theoretical equations. To minimize the effect by the reflected wave, a mass-weighted damping zone is set at the right end of the NWT, and therefore, stable and uniform waves are simulated. The waves are generated using the linear and Stokes wave theories, and it is observed that the numerical wave profiles calculated by the Stokes wave theory yield high accuracy. When the relative depth is smaller than two, the results show good agreement irrespective of the wave steepness. However, when the relative depth and wave steepness are larger than 2 and 0.04, respectively, the errors are negligible if the measurement position is close to the excitation plate. However, the error is 10% or larger if the measurement position is away from the excitation location. Applicable target wave ranges are confirmed through various case studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.352-361
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• 2021

An underwater positioning method that can be applied to structures for underwater construction is being developed at the Korea Institute of Ocean Science and Technology. The method uses an extended Kalman filter (EKF) based on an inertial navigation system for precise and continuous position estimation. The observation matrix was configured to be variable in order to apply asynchronous measured sensor data in the correction step of the EKF. A Doppler velocity logger (DVL) can acquire signals only when attached to the bottom of an underwater structure, and it is difficult to install and recover. Therefore, a complex sensor device for underwater structure attachment was developed without a DVL in consideration of an underwater construction environment, installation location, system operation convenience, etc.. Its performance was verified through a water tank test. The results are the measured underwater position using an ultra-short baseline, the estimated position using only a position vector, and the estimated position using position/velocity vectors. The results were compared and evaluated using the circular error probability (CEP). As a result, the CEP of the USBL alone was 0.02 m, the CEP of the position estimation with only the position vector corrected was 3.76 m, and the CEP of the position estimation with the position and velocity vectors corrected was 0.06 m. Through this research, it was confirmed that stable underwater positioning can be carried out using asynchronous sensors without a DVL.